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		<id>https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10493</id>
		<title>2014:Audio Downbeat Estimation</title>
		<link rel="alternate" type="text/html" href="https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10493"/>
		<updated>2014-10-06T16:14:32Z</updated>

		<summary type="html">&lt;p&gt;Matthew Davies: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Description ==&lt;br /&gt;
&lt;br /&gt;
'''This task is new for 2014!'''&lt;br /&gt;
&lt;br /&gt;
This text has been adapted from the Audio Beat Tracking Wiki page.  Please add your comments and discussion at the bottom of this page.&lt;br /&gt;
&lt;br /&gt;
The aim of the automatic downbeat estimation task is to identify the locations of downbeats in a collection of sound files. While this is similar to the Audio Beat Tracking task, here the aim is to find the first beat of each bar (measure) rather than all beat times. Algorithms are '''not''' required to estimate beat times or time-signature in addition to downbeats.&lt;br /&gt;
&lt;br /&gt;
Submitted algorithms will be evaluated in terms of their accuracy in finding downbeat locations (only) as annotated by musical experts across several diverse datasets.&lt;br /&gt;
&lt;br /&gt;
'''Update 22/07/14''' A small set of training data is now available. Please see [[#Example_Data]]&lt;br /&gt;
&lt;br /&gt;
== Data ==&lt;br /&gt;
&lt;br /&gt;
=== Collections ===&lt;br /&gt;
'''Ballroom'''&lt;br /&gt;
The ballroom dataset contains eight different dance styles (Cha Cha, Jive, Quickstep, Rumba, Samba, Tango, Viennese Waltz and Waltz). It consists of '''697''' excerpts of 30s in duration.&lt;br /&gt;
The dataset contains two different meters 3/4 and 4/4 - but all pieces have constant meter. For further information see Dixon et al (2004) and Krebs et al (2013).&lt;br /&gt;
Note, we are using the ground truth annotations from Krebs et al. (2013) available at https://github.com/CPJKU/BallroomAnnotations&lt;br /&gt;
&lt;br /&gt;
'''Isophonics (Beatles only)'''&lt;br /&gt;
The Beatles dataset from the Centre for Digital Music at Queen Mary, University of London (http://www.isophonics.net/), as also used for Audio Chord Estimation in MIREX for many years. &lt;br /&gt;
This dataset contains '''179''' complete songs (all except Revolution 9), the majority of which are in 4/4.&lt;br /&gt;
For further information see Mauch et al (2009).&lt;br /&gt;
&lt;br /&gt;
'''Turkish Data'''&lt;br /&gt;
The Turkish corpus is an extended version of the annotated data used in Srinivasamurthy et al. (2014). It includes '''82''' excerpts of one&lt;br /&gt;
minute length each, and each piece belongs to one of three&lt;br /&gt;
rhythm classes that are referred to as usul in Turkish Art&lt;br /&gt;
music. 32 pieces are in the 9/8-usul Aksak, 20 pieces&lt;br /&gt;
in the 10/8-usul Curcuna, 30 samples in the 8/8-usul&lt;br /&gt;
Düyek.&lt;br /&gt;
&lt;br /&gt;
'''Cretan Data'''&lt;br /&gt;
The corpus of Cretan music consists of '''42''' full length pieces of Cretan leaping dances. While there are several dances that differ in terms of their steps, the differences in&lt;br /&gt;
the sound are most noticeable in the melodic content, and all pieces can be considered to belong to one rhythmic style. All these dances are usually notated using a 2/4 time signature,&lt;br /&gt;
and the accompanying rhythmical patterns are usually played on a Cretan lute. While a variety of rhythmic patterns exist, they do not relate to a specific dance and can be&lt;br /&gt;
assumed to occur in all of the 42 songs in this corpus.&lt;br /&gt;
&lt;br /&gt;
'''Carnatic Data'''&lt;br /&gt;
The Carnatic music dataset is a subset of the CompMusic [http://compmusic.upf.edu/carnatic-rhythm-dataset Carnatic Music Rhythm Dataset]. It includes '''118''' two minute long excerpts spanning four most commonly used tālas (the rhythmic framework of Carnatic music, consisting of time cycles) of Carnatic music. There are 30 examples in each of ādi tāla (8 beats/cycle), rūpaka tāla (3 beats/cycle) and miśra chāpu tāla (7 beats/cycle), and 28 examples in khaṇḍa chāpu tāla (5 beats/cycle). The beats of the tāla in miśra chāpu and khaṇḍa chāpu are non-uniform, but for consistency with other datasets, a uniform beat pulse was obtained by interpolating the non-uniformly spaced beat locations. The recordings consist of both vocal and instrumental music recordings representative of the present day performance practice. All recordings contain percussion accompaniment, mainly the Mridangam. &lt;br /&gt;
&lt;br /&gt;
'''HJDB''' (to be confirmed)&lt;br /&gt;
The HJDB dataset contains '''236''' excerpts of Hardcore, Jungle and Drum and Bass music between 30s and 2 minutes in length. All excerpts are in 4/4 and have a constant tempo. &lt;br /&gt;
For further information see Hockman et al (2012).&lt;br /&gt;
&lt;br /&gt;
In total this makes '''1354''' excerpts (of which 259 are full length songs).&lt;br /&gt;
&lt;br /&gt;
=== Audio Formats ===&lt;br /&gt;
&lt;br /&gt;
The data are monophonic sound files&lt;br /&gt;
&lt;br /&gt;
* CD-quality (PCM, 16-bit, 44100 Hz) for all except Ballroom (originally lower quality, but resampled to 44100 Hz)&lt;br /&gt;
* single channel (mono)&lt;br /&gt;
&lt;br /&gt;
== Submission Format ==&lt;br /&gt;
Submissions to this task will have to conform to a specified format detailed below. Submissions should be packaged and contain at least two files: The algorithm itself and a README containing contact information and detailing, in full, the use of the algorithm.&lt;br /&gt;
&lt;br /&gt;
=== Input Data ===&lt;br /&gt;
Participating algorithms will have to read audio in the following format:&lt;br /&gt;
&lt;br /&gt;
* Sample rate: 44.1 KHz&lt;br /&gt;
* Sample size: 16 bit&lt;br /&gt;
* Number of channels: 1 (mono)&lt;br /&gt;
* Encoding: WAV &lt;br /&gt;
&lt;br /&gt;
=== Output Data ===&lt;br /&gt;
&lt;br /&gt;
The downbeat estimation algorithms will return downbeat times in an ASCII text file for each input .wav audio file. The specification of this output file is immediately below.&lt;br /&gt;
&lt;br /&gt;
=== Output File Format (Audio Downbeat Estimation) ===&lt;br /&gt;
&lt;br /&gt;
The downbeat output file format is an ASCII text format. Each downbeat time is specified, in seconds, on its own line. Specifically, &lt;br /&gt;
&lt;br /&gt;
 &amp;lt;downbeat time (in seconds)&amp;gt;\n&lt;br /&gt;
&lt;br /&gt;
where \n denotes the end of line. The &amp;lt; and &amp;gt; characters are not included. An example output file would look something like:&lt;br /&gt;
&lt;br /&gt;
 0.243&lt;br /&gt;
 1.486&lt;br /&gt;
 2.729&lt;br /&gt;
&lt;br /&gt;
=== Algorithm Calling Format ===&lt;br /&gt;
&lt;br /&gt;
The submitted algorithm must take as arguments a SINGLE .wav file to perform the downbeat estimation as well as the full output path and filename of the output file. The ability to specify the output path and file name is essential. Denoting the input .wav file path and name as %input and the output file path and name as %output, a program called foobar could be called from the command-line as follows:&lt;br /&gt;
&lt;br /&gt;
 foobar %input %output&lt;br /&gt;
 foobar -i %input -o %output&lt;br /&gt;
&lt;br /&gt;
Moreover, if your submission takes additional parameters, such as a detection threshold, foobar could be called like:&lt;br /&gt;
&lt;br /&gt;
 foobar .1 %input %output&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output  &lt;br /&gt;
&lt;br /&gt;
If your submission is in MATLAB, it should be submitted as a function. Once again, the function must contain String inputs for the full path and names of the input and output files. Parameters could also be specified as input arguments of the function. For example: &lt;br /&gt;
&lt;br /&gt;
 foobar('%input','%output')&lt;br /&gt;
 foobar(.1,'%input','%output')&lt;br /&gt;
&lt;br /&gt;
=== README File ===&lt;br /&gt;
&lt;br /&gt;
A README file accompanying each submission should contain explicit instructions on how to to run the program (as well as contact information, etc.). In particular, each command line to run should be specified, using %input for the input sound file and %output for the resulting text file.&lt;br /&gt;
&lt;br /&gt;
For instance, to test the program foobar with different values for parameters param1, the README file would look like:&lt;br /&gt;
&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output&lt;br /&gt;
 foobar -param1 .15 -i %input -o %output&lt;br /&gt;
 foobar -param1 .2 -i %input -o %output&lt;br /&gt;
 foobar -param1 .25 -i %input -o %output&lt;br /&gt;
 foobar -param1 .3 -i %input -o %output&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
For a submission using MATLAB, the README file could look like:&lt;br /&gt;
&lt;br /&gt;
 matlab -r &amp;quot;foobar(.1,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.15,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.2,'%input','%output');quit;&amp;quot; &lt;br /&gt;
 matlab -r &amp;quot;foobar(.25,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.3,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
The different command lines to evaluate the performance of each parameter set over the whole database will be generated automatically from each line in the README file containing both '%input' and '%output' strings.&lt;br /&gt;
&lt;br /&gt;
== Evaluation Procedure ==&lt;br /&gt;
&lt;br /&gt;
For the evalution procedure we will use&lt;br /&gt;
*'''F-measure''' - the standard calculation as used in onset and beat tracking evaluation with a +/-70ms window, see Dixon (2007).&lt;br /&gt;
&lt;br /&gt;
Given the high diversity of musical styles included in the task, results will be reported per each invidiual dataset. &lt;br /&gt;
&lt;br /&gt;
== Example Data ==&lt;br /&gt;
&lt;br /&gt;
A total of 20 beat and downbeat annotated 30s excerpts are available for participants. &lt;br /&gt;
The data (47MB) is available to download here:&lt;br /&gt;
&lt;br /&gt;
https://www.music-ir.org/evaluation/MIREX/data/2014/downbeat/&lt;br /&gt;
&lt;br /&gt;
''User:'' downbeat &lt;br /&gt;
&lt;br /&gt;
''Password:'' d0wn63at&lt;br /&gt;
&lt;br /&gt;
Due to the availability of the Ballroom and Beatles datasets, we only include examples of the remaining styles as follows:&lt;br /&gt;
&lt;br /&gt;
'''HJDB''': 5 excerpts&lt;br /&gt;
&lt;br /&gt;
'''Cretan''': 3 excerpts&lt;br /&gt;
&lt;br /&gt;
'''Carnatic''': 4 excerpts&lt;br /&gt;
&lt;br /&gt;
'''Turkish''': 8 excerpts (2x Aksak, 2x Curcuna, 2x Düyek, and 2x Sofyan).&lt;br /&gt;
&lt;br /&gt;
Please note the audio files are in ''.flac'' format.&lt;br /&gt;
&lt;br /&gt;
For each audio file, e.g. ''hjdb1.fla''c there is a corresponding annotation file ''hjdb1.txt'' &lt;br /&gt;
&lt;br /&gt;
Each ''.txt'' file contains timestamps corresponding to beat annotations and a label to denote the position in the bar. &lt;br /&gt;
&lt;br /&gt;
All beat times labelled '1' correspond to downbeats. &lt;br /&gt;
&lt;br /&gt;
== Time and hardware limits ==&lt;br /&gt;
Due to the potentially high number of participants in this and other audio tasks, hard limits on the runtime of submissions will be imposed.&lt;br /&gt;
&lt;br /&gt;
A hard limit of 24 hours will be imposed on analysis times. Submissions exceeding this limit may not receive a result.&lt;br /&gt;
&lt;br /&gt;
== Potential Participants ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
Jose R. Zapata / joser.zapata (at) upb.edu.co&lt;br /&gt;
&lt;br /&gt;
== Discussion ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
= Bibliography =&lt;br /&gt;
&lt;br /&gt;
S. Dixon, F. Gouyon and G. Widmer, [http://ismir2004.ismir.net/proceedings/p093-page-509-paper165.pdf Towards Characterisation of Music via Rhythmic Patterns], In Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR 2004), pp 509-516.&lt;br /&gt;
&lt;br /&gt;
S. Dixon, [http://www.eecs.qmul.ac.uk/~simond/pub/2007/jnmr07.pdf Evaluation of audio beat tracking system BeatRoot], Journal of New Music Research, vol. 36, no. 1, pp. 39-51, 2007.&lt;br /&gt;
&lt;br /&gt;
J. A. Hockman, M. E. P. Davies, I. Fujinaga.[http://ismir2012.ismir.net/event/papers/169-ismir-2012.pdf ONE IN THE JUNGLE: Downbeat Detection in Hardcore, Jungle, and Drum and Bass], In Proceedings of 13th International Society for Music Information Retrieval Conference (ISMIR), Porto, Portugal pp. 169-174, 2012.&lt;br /&gt;
&lt;br /&gt;
F. Krebs, S. Boeck, and G. Widmer, [http://www.cp.jku.at/research/papers/Krebs_etal_ISMIR_2013.pdf Rhythmic Pattern Modeling for Beat- and Downbeat Tracking in Musical Audio], In Proceedings of 14th International Society for Music Information Retrieval Conference (ISMIR), Curitiba, Brazil, 2013.&lt;br /&gt;
&lt;br /&gt;
M. Mauch, C. Cannam, M. E. P. Davies, S. Dixon, C. Harte, S. Kolozali and D. Tidhar, [http://ismir2009.ismir.net/proceedings/LBD-18.pdf OMRAS2 Metadata Project 2009], Late-breaking session at the 10th International Conference on Music Information Retrieval, 2009.&lt;br /&gt;
&lt;br /&gt;
A. Srinivasamurthy, A. Holzapfel, and Xavier Serra, [http://www.tandfonline.com/doi/full/10.1080/09298215.2013.879902 In Search of Automatic Rhythm Analysis Methods for Turkish and Indian Art Music], Journal of New Music Research, vol. 43, no. 1, pp. 94-114, 2014.&lt;/div&gt;</summary>
		<author><name>Matthew Davies</name></author>
		
	</entry>
	<entry>
		<id>https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10300</id>
		<title>2014:Audio Downbeat Estimation</title>
		<link rel="alternate" type="text/html" href="https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10300"/>
		<updated>2014-07-22T18:33:44Z</updated>

		<summary type="html">&lt;p&gt;Matthew Davies: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Description ==&lt;br /&gt;
&lt;br /&gt;
'''This task is new for 2014!'''&lt;br /&gt;
&lt;br /&gt;
This text has been adapted from the Audio Beat Tracking Wiki page.  Please add your comments and discussion at the bottom of this page.&lt;br /&gt;
&lt;br /&gt;
The aim of the automatic downbeat estimation task is to identify the locations of downbeats in a collection of sound files. While this is similar to the Audio Beat Tracking task, here the aim is to find the first beat of each bar (measure) rather than all beat times. Algorithms are '''not''' required to estimate beat times or time-signature in addition to downbeats.&lt;br /&gt;
&lt;br /&gt;
Submitted algorithms will be evaluated in terms of their accuracy in finding downbeat locations (only) as annotated by musical experts across several diverse datasets.&lt;br /&gt;
&lt;br /&gt;
'''Update 22/07/14''' A small set of training data is now available. Please see [[#Example_Data]]&lt;br /&gt;
&lt;br /&gt;
== Data ==&lt;br /&gt;
&lt;br /&gt;
=== Collections ===&lt;br /&gt;
'''Ballroom'''&lt;br /&gt;
The ballroom dataset contains eight different dance styles (Cha Cha, Jive, Quickstep, Rumba, Samba, Tango, Viennese Waltz and Waltz). It consists of '''697''' excerpts of 30s in duration.&lt;br /&gt;
The dataset contains two different meters 3/4 and 4/4 - but all pieces have constant meter. For further information see Dixon et al (2004) and Krebs et al (2013).&lt;br /&gt;
Note, we are using the ground truth annotations from Krebs et al. (2013) available at https://github.com/CPJKU/BallroomAnnotations&lt;br /&gt;
&lt;br /&gt;
'''Isophonics (Beatles only)'''&lt;br /&gt;
The Beatles dataset from the Centre for Digital Music at Queen Mary, University of London (http://www.isophonics.net/), as also used for Audio Chord Estimation in MIREX for many years. &lt;br /&gt;
This dataset contains '''179''' complete songs (all except Revolution 9), the majority of which are in 4/4.&lt;br /&gt;
For further information see Mauch et al (2009).&lt;br /&gt;
&lt;br /&gt;
'''Turkish Data'''&lt;br /&gt;
The Turkish corpus is an extended version of the annotated data used in Srinivasamurthy et al. (2014). It includes '''85''' excerpts of one&lt;br /&gt;
minute length each, and each piece belongs to one of four&lt;br /&gt;
rhythm classes that are referred to as usul in Turkish Art&lt;br /&gt;
music. 30 pieces are in the 9/8-usul Aksak, 18 pieces&lt;br /&gt;
in the 10/8-usul Curcuna, 28 samples in the 8/8-usul&lt;br /&gt;
Düyek, and 9 samples in the 4/4 Sofyan.&lt;br /&gt;
&lt;br /&gt;
'''Cretan Data'''&lt;br /&gt;
The corpus of Cretan music consists of '''42''' full length pieces of Cretan leaping dances. While there are several dances that differ in terms of their steps, the differences in&lt;br /&gt;
the sound are most noticeable in the melodic content, and all pieces can be considered to belong to one rhythmic style. All these dances are usually notated using a 2/4 time signature,&lt;br /&gt;
and the accompanying rhythmical patterns are usually played on a Cretan lute. While a variety of rhythmic patterns exist, they do not relate to a specific dance and can be&lt;br /&gt;
assumed to occur in all of the 42 songs in this corpus.&lt;br /&gt;
&lt;br /&gt;
'''Carnatic Data'''&lt;br /&gt;
The Carnatic music dataset is a subset of the CompMusic [http://compmusic.upf.edu/carnatic-rhythm-dataset Carnatic Music Rhythm Dataset]. It includes '''118''' two minute long excerpts spanning four most commonly used tālas (the rhythmic framework of Carnatic music, consisting of time cycles) of Carnatic music. There are 30 examples in each of ādi tāla (8 beats/cycle), rūpaka tāla (3 beats/cycle) and miśra chāpu tāla (7 beats/cycle), and 28 examples in khaṇḍa chāpu tāla (5 beats/cycle). The beats of the tāla in miśra chāpu and khaṇḍa chāpu are non-uniform, but for consistency with other datasets, a uniform beat pulse was obtained by interpolating the non-uniformly spaced beat locations. The recordings consist of both vocal and instrumental music recordings representative of the present day performance practice. All recordings contain percussion accompaniment, mainly the Mridangam. &lt;br /&gt;
&lt;br /&gt;
'''HJDB''' (to be confirmed)&lt;br /&gt;
The HJDB dataset contains '''236''' excerpts of Hardcore, Jungle and Drum and Bass music between 30s and 2 minutes in length. All excerpts are in 4/4 and have a constant tempo. &lt;br /&gt;
For further information see Hockman et al (2012).&lt;br /&gt;
&lt;br /&gt;
In total this makes '''1357''' excerpts (of which 259 are full length songs).&lt;br /&gt;
&lt;br /&gt;
=== Audio Formats ===&lt;br /&gt;
&lt;br /&gt;
The data are monophonic sound files&lt;br /&gt;
&lt;br /&gt;
* CD-quality (PCM, 16-bit, 44100 Hz) for all except Ballroom (originally lower quality, but resampled to 44100 Hz)&lt;br /&gt;
* single channel (mono)&lt;br /&gt;
&lt;br /&gt;
== Submission Format ==&lt;br /&gt;
Submissions to this task will have to conform to a specified format detailed below. Submissions should be packaged and contain at least two files: The algorithm itself and a README containing contact information and detailing, in full, the use of the algorithm.&lt;br /&gt;
&lt;br /&gt;
=== Input Data ===&lt;br /&gt;
Participating algorithms will have to read audio in the following format:&lt;br /&gt;
&lt;br /&gt;
* Sample rate: 44.1 KHz&lt;br /&gt;
* Sample size: 16 bit&lt;br /&gt;
* Number of channels: 1 (mono)&lt;br /&gt;
* Encoding: WAV &lt;br /&gt;
&lt;br /&gt;
=== Output Data ===&lt;br /&gt;
&lt;br /&gt;
The downbeat estimation algorithms will return downbeat times in an ASCII text file for each input .wav audio file. The specification of this output file is immediately below.&lt;br /&gt;
&lt;br /&gt;
=== Output File Format (Audio Downbeat Estimation) ===&lt;br /&gt;
&lt;br /&gt;
The downbeat output file format is an ASCII text format. Each downbeat time is specified, in seconds, on its own line. Specifically, &lt;br /&gt;
&lt;br /&gt;
 &amp;lt;downbeat time (in seconds)&amp;gt;\n&lt;br /&gt;
&lt;br /&gt;
where \n denotes the end of line. The &amp;lt; and &amp;gt; characters are not included. An example output file would look something like:&lt;br /&gt;
&lt;br /&gt;
 0.243&lt;br /&gt;
 1.486&lt;br /&gt;
 2.729&lt;br /&gt;
&lt;br /&gt;
=== Algorithm Calling Format ===&lt;br /&gt;
&lt;br /&gt;
The submitted algorithm must take as arguments a SINGLE .wav file to perform the downbeat estimation as well as the full output path and filename of the output file. The ability to specify the output path and file name is essential. Denoting the input .wav file path and name as %input and the output file path and name as %output, a program called foobar could be called from the command-line as follows:&lt;br /&gt;
&lt;br /&gt;
 foobar %input %output&lt;br /&gt;
 foobar -i %input -o %output&lt;br /&gt;
&lt;br /&gt;
Moreover, if your submission takes additional parameters, such as a detection threshold, foobar could be called like:&lt;br /&gt;
&lt;br /&gt;
 foobar .1 %input %output&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output  &lt;br /&gt;
&lt;br /&gt;
If your submission is in MATLAB, it should be submitted as a function. Once again, the function must contain String inputs for the full path and names of the input and output files. Parameters could also be specified as input arguments of the function. For example: &lt;br /&gt;
&lt;br /&gt;
 foobar('%input','%output')&lt;br /&gt;
 foobar(.1,'%input','%output')&lt;br /&gt;
&lt;br /&gt;
=== README File ===&lt;br /&gt;
&lt;br /&gt;
A README file accompanying each submission should contain explicit instructions on how to to run the program (as well as contact information, etc.). In particular, each command line to run should be specified, using %input for the input sound file and %output for the resulting text file.&lt;br /&gt;
&lt;br /&gt;
For instance, to test the program foobar with different values for parameters param1, the README file would look like:&lt;br /&gt;
&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output&lt;br /&gt;
 foobar -param1 .15 -i %input -o %output&lt;br /&gt;
 foobar -param1 .2 -i %input -o %output&lt;br /&gt;
 foobar -param1 .25 -i %input -o %output&lt;br /&gt;
 foobar -param1 .3 -i %input -o %output&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
For a submission using MATLAB, the README file could look like:&lt;br /&gt;
&lt;br /&gt;
 matlab -r &amp;quot;foobar(.1,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.15,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.2,'%input','%output');quit;&amp;quot; &lt;br /&gt;
 matlab -r &amp;quot;foobar(.25,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.3,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
The different command lines to evaluate the performance of each parameter set over the whole database will be generated automatically from each line in the README file containing both '%input' and '%output' strings.&lt;br /&gt;
&lt;br /&gt;
== Evaluation Procedure ==&lt;br /&gt;
&lt;br /&gt;
For the evalution procedure we will use&lt;br /&gt;
*'''F-measure''' - the standard calculation as used in onset and beat tracking evaluation with a +/-70ms window, see Dixon (2007).&lt;br /&gt;
&lt;br /&gt;
Given the high diversity of musical styles included in the task, results will be reported per each invidiual dataset. &lt;br /&gt;
&lt;br /&gt;
== Example Data ==&lt;br /&gt;
&lt;br /&gt;
A total of 20 beat and downbeat annotated 30s excerpts are available for participants. &lt;br /&gt;
The data (47MB) is available to download here:&lt;br /&gt;
&lt;br /&gt;
https://www.music-ir.org/evaluation/MIREX/data/2014/downbeat/&lt;br /&gt;
&lt;br /&gt;
''User:'' downbeat &lt;br /&gt;
&lt;br /&gt;
''Password:'' d0wn63at&lt;br /&gt;
&lt;br /&gt;
Due to the availability of the Ballroom and Beatles datasets, we only include examples of the remaining styles as follows:&lt;br /&gt;
&lt;br /&gt;
'''HJDB''': 5 excerpts&lt;br /&gt;
&lt;br /&gt;
'''Cretan''': 3 excerpts&lt;br /&gt;
&lt;br /&gt;
'''Carnatic''': 4 excerpts&lt;br /&gt;
&lt;br /&gt;
'''Turkish''': 8 excerpts (2x Aksak, 2x Curcuna, 2x Düyek, and 2x Sofyan).&lt;br /&gt;
&lt;br /&gt;
Please note the audio files are in ''.flac'' format.&lt;br /&gt;
&lt;br /&gt;
For each audio file, e.g. ''hjdb1.fla''c there is a corresponding annotation file ''hjdb1.txt'' &lt;br /&gt;
&lt;br /&gt;
Each ''.txt'' file contains timestamps corresponding to beat annotations and a label to denote the position in the bar. &lt;br /&gt;
&lt;br /&gt;
All beat times labelled '1' correspond to downbeats. &lt;br /&gt;
&lt;br /&gt;
== Time and hardware limits ==&lt;br /&gt;
Due to the potentially high number of participants in this and other audio tasks, hard limits on the runtime of submissions will be imposed.&lt;br /&gt;
&lt;br /&gt;
A hard limit of 24 hours will be imposed on analysis times. Submissions exceeding this limit may not receive a result.&lt;br /&gt;
&lt;br /&gt;
== Potential Participants ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
Jose R. Zapata / joser.zapata (at) upb.edu.co&lt;br /&gt;
&lt;br /&gt;
== Discussion ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
= Bibliography =&lt;br /&gt;
&lt;br /&gt;
S. Dixon, F. Gouyon and G. Widmer, [http://ismir2004.ismir.net/proceedings/p093-page-509-paper165.pdf Towards Characterisation of Music via Rhythmic Patterns], In Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR 2004), pp 509-516.&lt;br /&gt;
&lt;br /&gt;
S. Dixon, [http://www.eecs.qmul.ac.uk/~simond/pub/2007/jnmr07.pdf Evaluation of audio beat tracking system BeatRoot], Journal of New Music Research, vol. 36, no. 1, pp. 39-51, 2007.&lt;br /&gt;
&lt;br /&gt;
J. A. Hockman, M. E. P. Davies, I. Fujinaga.[http://ismir2012.ismir.net/event/papers/169-ismir-2012.pdf ONE IN THE JUNGLE: Downbeat Detection in Hardcore, Jungle, and Drum and Bass], In Proceedings of 13th International Society for Music Information Retrieval Conference (ISMIR), Porto, Portugal pp. 169-174, 2012.&lt;br /&gt;
&lt;br /&gt;
F. Krebs, S. Boeck, and G. Widmer, [http://www.cp.jku.at/research/papers/Krebs_etal_ISMIR_2013.pdf Rhythmic Pattern Modeling for Beat- and Downbeat Tracking in Musical Audio], In Proceedings of 14th International Society for Music Information Retrieval Conference (ISMIR), Curitiba, Brazil, 2013.&lt;br /&gt;
&lt;br /&gt;
M. Mauch, C. Cannam, M. E. P. Davies, S. Dixon, C. Harte, S. Kolozali and D. Tidhar, [http://ismir2009.ismir.net/proceedings/LBD-18.pdf OMRAS2 Metadata Project 2009], Late-breaking session at the 10th International Conference on Music Information Retrieval, 2009.&lt;br /&gt;
&lt;br /&gt;
A. Srinivasamurthy, A. Holzapfel, and Xavier Serra, [http://www.tandfonline.com/doi/full/10.1080/09298215.2013.879902 In Search of Automatic Rhythm Analysis Methods for Turkish and Indian Art Music], Journal of New Music Research, vol. 43, no. 1, pp. 94-114, 2014.&lt;/div&gt;</summary>
		<author><name>Matthew Davies</name></author>
		
	</entry>
	<entry>
		<id>https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10299</id>
		<title>2014:Audio Downbeat Estimation</title>
		<link rel="alternate" type="text/html" href="https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10299"/>
		<updated>2014-07-22T18:32:38Z</updated>

		<summary type="html">&lt;p&gt;Matthew Davies: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Description ==&lt;br /&gt;
&lt;br /&gt;
'''This task is new for 2014!'''&lt;br /&gt;
&lt;br /&gt;
This text has been adapted from the Audio Beat Tracking Wiki page.  Please add your comments and discussion at the bottom of this page.&lt;br /&gt;
&lt;br /&gt;
The aim of the automatic downbeat estimation task is to identify the locations of downbeats in a collection of sound files. While this is similar to the Audio Beat Tracking task, here the aim is to find the first beat of each bar (measure) rather than all beat times. Algorithms are '''not''' required to estimate beat times or time-signature in addition to downbeats.&lt;br /&gt;
&lt;br /&gt;
Submitted algorithms will be evaluated in terms of their accuracy in finding downbeat locations (only) as annotated by musical experts across several diverse datasets.&lt;br /&gt;
&lt;br /&gt;
'''Update 22/07/14''' A small set of training data is now available. Please see [[#Example_Data]]&lt;br /&gt;
&lt;br /&gt;
== Data ==&lt;br /&gt;
&lt;br /&gt;
=== Collections ===&lt;br /&gt;
'''Ballroom'''&lt;br /&gt;
The ballroom dataset contains eight different dance styles (Cha Cha, Jive, Quickstep, Rumba, Samba, Tango, Viennese Waltz and Waltz). It consists of '''697''' excerpts of 30s in duration.&lt;br /&gt;
The dataset contains two different meters 3/4 and 4/4 - but all pieces have constant meter. For further information see Dixon et al (2004) and Krebs et al (2013).&lt;br /&gt;
Note, we are using the ground truth annotations from Krebs et al. (2013) available at https://github.com/CPJKU/BallroomAnnotations&lt;br /&gt;
&lt;br /&gt;
'''Isophonics (Beatles only)'''&lt;br /&gt;
The Beatles dataset from the Centre for Digital Music at Queen Mary, University of London (http://www.isophonics.net/), as also used for Audio Chord Estimation in MIREX for many years. &lt;br /&gt;
This dataset contains '''179''' complete songs (all except Revolution 9), the majority of which are in 4/4.&lt;br /&gt;
For further information see Mauch et al (2009).&lt;br /&gt;
&lt;br /&gt;
'''Turkish Data'''&lt;br /&gt;
The Turkish corpus is an extended version of the annotated data used in Srinivasamurthy et al. (2014). It includes '''85''' excerpts of one&lt;br /&gt;
minute length each, and each piece belongs to one of four&lt;br /&gt;
rhythm classes that are referred to as usul in Turkish Art&lt;br /&gt;
music. 30 pieces are in the 9/8-usul Aksak, 18 pieces&lt;br /&gt;
in the 10/8-usul Curcuna, 28 samples in the 8/8-usul&lt;br /&gt;
Düyek, and 9 samples in the 4/4 Sofyan.&lt;br /&gt;
&lt;br /&gt;
'''Cretan Data'''&lt;br /&gt;
The corpus of Cretan music consists of '''42''' full length pieces of Cretan leaping dances. While there are several dances that differ in terms of their steps, the differences in&lt;br /&gt;
the sound are most noticeable in the melodic content, and all pieces can be considered to belong to one rhythmic style. All these dances are usually notated using a 2/4 time signature,&lt;br /&gt;
and the accompanying rhythmical patterns are usually played on a Cretan lute. While a variety of rhythmic patterns exist, they do not relate to a specific dance and can be&lt;br /&gt;
assumed to occur in all of the 42 songs in this corpus.&lt;br /&gt;
&lt;br /&gt;
'''Carnatic Data'''&lt;br /&gt;
The Carnatic music dataset is a subset of the CompMusic [http://compmusic.upf.edu/carnatic-rhythm-dataset Carnatic Music Rhythm Dataset]. It includes '''118''' two minute long excerpts spanning four most commonly used tālas (the rhythmic framework of Carnatic music, consisting of time cycles) of Carnatic music. There are 30 examples in each of ādi tāla (8 beats/cycle), rūpaka tāla (3 beats/cycle) and miśra chāpu tāla (7 beats/cycle), and 28 examples in khaṇḍa chāpu tāla (5 beats/cycle). The beats of the tāla in miśra chāpu and khaṇḍa chāpu are non-uniform, but for consistency with other datasets, a uniform beat pulse was obtained by interpolating the non-uniformly spaced beat locations. The recordings consist of both vocal and instrumental music recordings representative of the present day performance practice. All recordings contain percussion accompaniment, mainly the Mridangam. &lt;br /&gt;
&lt;br /&gt;
'''HJDB''' (to be confirmed)&lt;br /&gt;
The HJDB dataset contains '''236''' excerpts of Hardcore, Jungle and Drum and Bass music between 30s and 2 minutes in length. All excerpts are in 4/4 and have a constant tempo. &lt;br /&gt;
For further information see Hockman et al (2012).&lt;br /&gt;
&lt;br /&gt;
In total this makes '''1357''' excerpts (of which 259 are full length songs).&lt;br /&gt;
&lt;br /&gt;
=== Audio Formats ===&lt;br /&gt;
&lt;br /&gt;
The data are monophonic sound files&lt;br /&gt;
&lt;br /&gt;
* CD-quality (PCM, 16-bit, 44100 Hz) for all except Ballroom (originally lower quality, but resampled to 44100 Hz)&lt;br /&gt;
* single channel (mono)&lt;br /&gt;
&lt;br /&gt;
== Submission Format ==&lt;br /&gt;
Submissions to this task will have to conform to a specified format detailed below. Submissions should be packaged and contain at least two files: The algorithm itself and a README containing contact information and detailing, in full, the use of the algorithm.&lt;br /&gt;
&lt;br /&gt;
=== Input Data ===&lt;br /&gt;
Participating algorithms will have to read audio in the following format:&lt;br /&gt;
&lt;br /&gt;
* Sample rate: 44.1 KHz&lt;br /&gt;
* Sample size: 16 bit&lt;br /&gt;
* Number of channels: 1 (mono)&lt;br /&gt;
* Encoding: WAV &lt;br /&gt;
&lt;br /&gt;
=== Output Data ===&lt;br /&gt;
&lt;br /&gt;
The downbeat estimation algorithms will return downbeat times in an ASCII text file for each input .wav audio file. The specification of this output file is immediately below.&lt;br /&gt;
&lt;br /&gt;
=== Output File Format (Audio Downbeat Estimation) ===&lt;br /&gt;
&lt;br /&gt;
The downbeat output file format is an ASCII text format. Each downbeat time is specified, in seconds, on its own line. Specifically, &lt;br /&gt;
&lt;br /&gt;
 &amp;lt;downbeat time (in seconds)&amp;gt;\n&lt;br /&gt;
&lt;br /&gt;
where \n denotes the end of line. The &amp;lt; and &amp;gt; characters are not included. An example output file would look something like:&lt;br /&gt;
&lt;br /&gt;
 0.243&lt;br /&gt;
 1.486&lt;br /&gt;
 2.729&lt;br /&gt;
&lt;br /&gt;
=== Algorithm Calling Format ===&lt;br /&gt;
&lt;br /&gt;
The submitted algorithm must take as arguments a SINGLE .wav file to perform the downbeat estimation as well as the full output path and filename of the output file. The ability to specify the output path and file name is essential. Denoting the input .wav file path and name as %input and the output file path and name as %output, a program called foobar could be called from the command-line as follows:&lt;br /&gt;
&lt;br /&gt;
 foobar %input %output&lt;br /&gt;
 foobar -i %input -o %output&lt;br /&gt;
&lt;br /&gt;
Moreover, if your submission takes additional parameters, such as a detection threshold, foobar could be called like:&lt;br /&gt;
&lt;br /&gt;
 foobar .1 %input %output&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output  &lt;br /&gt;
&lt;br /&gt;
If your submission is in MATLAB, it should be submitted as a function. Once again, the function must contain String inputs for the full path and names of the input and output files. Parameters could also be specified as input arguments of the function. For example: &lt;br /&gt;
&lt;br /&gt;
 foobar('%input','%output')&lt;br /&gt;
 foobar(.1,'%input','%output')&lt;br /&gt;
&lt;br /&gt;
=== README File ===&lt;br /&gt;
&lt;br /&gt;
A README file accompanying each submission should contain explicit instructions on how to to run the program (as well as contact information, etc.). In particular, each command line to run should be specified, using %input for the input sound file and %output for the resulting text file.&lt;br /&gt;
&lt;br /&gt;
For instance, to test the program foobar with different values for parameters param1, the README file would look like:&lt;br /&gt;
&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output&lt;br /&gt;
 foobar -param1 .15 -i %input -o %output&lt;br /&gt;
 foobar -param1 .2 -i %input -o %output&lt;br /&gt;
 foobar -param1 .25 -i %input -o %output&lt;br /&gt;
 foobar -param1 .3 -i %input -o %output&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
For a submission using MATLAB, the README file could look like:&lt;br /&gt;
&lt;br /&gt;
 matlab -r &amp;quot;foobar(.1,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.15,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.2,'%input','%output');quit;&amp;quot; &lt;br /&gt;
 matlab -r &amp;quot;foobar(.25,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.3,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
The different command lines to evaluate the performance of each parameter set over the whole database will be generated automatically from each line in the README file containing both '%input' and '%output' strings.&lt;br /&gt;
&lt;br /&gt;
== Evaluation Procedures ==&lt;br /&gt;
&lt;br /&gt;
For the evalution procedure we will use&lt;br /&gt;
*'''F-measure''' - the standard calculation as used in onset and beat tracking evaluation with a +/-70ms window, see Dixon (2007).&lt;br /&gt;
&lt;br /&gt;
Given the high diversity of musical styles included in the task, results will be reported per each invidiual dataset. &lt;br /&gt;
&lt;br /&gt;
== Example Data ==&lt;br /&gt;
&lt;br /&gt;
A total of 20 beat and downbeat annotated 30s excerpts are available for participants. &lt;br /&gt;
The data (47MB) is available to download here:&lt;br /&gt;
&lt;br /&gt;
https://www.music-ir.org/evaluation/MIREX/data/2014/downbeat/&lt;br /&gt;
&lt;br /&gt;
''User:'' downbeat &lt;br /&gt;
&lt;br /&gt;
''Password:'' d0wn63at&lt;br /&gt;
&lt;br /&gt;
Due to the availability of the Ballroom and Beatles datasets, we only include examples of the remaining styles as follows:&lt;br /&gt;
&lt;br /&gt;
'''HJDB''': 5 excerpts&lt;br /&gt;
&lt;br /&gt;
'''Cretan''': 3 excerpts&lt;br /&gt;
&lt;br /&gt;
'''Carnatic''': 4 excerpts&lt;br /&gt;
&lt;br /&gt;
'''Turkish''': 8 excerpts (2x Aksak, 2x Curcuna, 2x Düyek, and 2x Sofyan).&lt;br /&gt;
&lt;br /&gt;
Please note the audio files are in ''.flac'' format.&lt;br /&gt;
&lt;br /&gt;
For each audio file, e.g. ''hjdb1.fla''c there is a corresponding annotation file ''hjdb1.txt'' &lt;br /&gt;
&lt;br /&gt;
Each ''.txt'' file contains timestamps corresponding to beat annotations and a label to denote the position in the bar. &lt;br /&gt;
&lt;br /&gt;
All beat times labelled '1' correspond to downbeats. &lt;br /&gt;
&lt;br /&gt;
== Time and hardware limits ==&lt;br /&gt;
Due to the potentially high number of participants in this and other audio tasks, hard limits on the runtime of submissions will be imposed.&lt;br /&gt;
&lt;br /&gt;
A hard limit of 24 hours will be imposed on analysis times. Submissions exceeding this limit may not receive a result.&lt;br /&gt;
&lt;br /&gt;
== Potential Participants ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
Jose R. Zapata / joser.zapata (at) upb.edu.co&lt;br /&gt;
&lt;br /&gt;
== Discussion ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
= Bibliography =&lt;br /&gt;
&lt;br /&gt;
S. Dixon, F. Gouyon and G. Widmer, [http://ismir2004.ismir.net/proceedings/p093-page-509-paper165.pdf Towards Characterisation of Music via Rhythmic Patterns], In Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR 2004), pp 509-516.&lt;br /&gt;
&lt;br /&gt;
S. Dixon, [http://www.eecs.qmul.ac.uk/~simond/pub/2007/jnmr07.pdf Evaluation of audio beat tracking system BeatRoot], Journal of New Music Research, vol. 36, no. 1, pp. 39-51, 2007.&lt;br /&gt;
&lt;br /&gt;
J. A. Hockman, M. E. P. Davies, I. Fujinaga.[http://ismir2012.ismir.net/event/papers/169-ismir-2012.pdf ONE IN THE JUNGLE: Downbeat Detection in Hardcore, Jungle, and Drum and Bass], In Proceedings of 13th International Society for Music Information Retrieval Conference (ISMIR), Porto, Portugal pp. 169-174, 2012.&lt;br /&gt;
&lt;br /&gt;
F. Krebs, S. Boeck, and G. Widmer, [http://www.cp.jku.at/research/papers/Krebs_etal_ISMIR_2013.pdf Rhythmic Pattern Modeling for Beat- and Downbeat Tracking in Musical Audio], In Proceedings of 14th International Society for Music Information Retrieval Conference (ISMIR), Curitiba, Brazil, 2013.&lt;br /&gt;
&lt;br /&gt;
M. Mauch, C. Cannam, M. E. P. Davies, S. Dixon, C. Harte, S. Kolozali and D. Tidhar, [http://ismir2009.ismir.net/proceedings/LBD-18.pdf OMRAS2 Metadata Project 2009], Late-breaking session at the 10th International Conference on Music Information Retrieval, 2009.&lt;br /&gt;
&lt;br /&gt;
A. Srinivasamurthy, A. Holzapfel, and Xavier Serra, [http://www.tandfonline.com/doi/full/10.1080/09298215.2013.879902 In Search of Automatic Rhythm Analysis Methods for Turkish and Indian Art Music], Journal of New Music Research, vol. 43, no. 1, pp. 94-114, 2014.&lt;/div&gt;</summary>
		<author><name>Matthew Davies</name></author>
		
	</entry>
	<entry>
		<id>https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10298</id>
		<title>2014:Audio Downbeat Estimation</title>
		<link rel="alternate" type="text/html" href="https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10298"/>
		<updated>2014-07-22T18:30:08Z</updated>

		<summary type="html">&lt;p&gt;Matthew Davies: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Description ==&lt;br /&gt;
&lt;br /&gt;
'''This task is new for 2014!'''&lt;br /&gt;
&lt;br /&gt;
This text has been adapted from the Audio Beat Tracking Wiki page.  Please add your comments and discussion at the bottom of this page.&lt;br /&gt;
&lt;br /&gt;
The aim of the automatic downbeat estimation task is to identify the locations of downbeats in a collection of sound files. While this is similar to the Audio Beat Tracking task, here the aim is to find the first beat of each bar (measure) rather than all beat times. Algorithms are '''not''' required to estimate beat times or time-signature in addition to downbeats.&lt;br /&gt;
&lt;br /&gt;
Submitted algorithms will be evaluated in terms of their accuracy in finding downbeat locations (only) as annotated by musical experts across several diverse datasets.&lt;br /&gt;
&lt;br /&gt;
'''Update 22/07/14''' A small set of training data is now available. Please see [[#Example_Data]]&lt;br /&gt;
&lt;br /&gt;
== Data ==&lt;br /&gt;
&lt;br /&gt;
=== Collections ===&lt;br /&gt;
'''Ballroom'''&lt;br /&gt;
The ballroom dataset contains eight different dance styles (Cha Cha, Jive, Quickstep, Rumba, Samba, Tango, Viennese Waltz and Waltz). It consists of '''697''' excerpts of 30s in duration.&lt;br /&gt;
The dataset contains two different meters 3/4 and 4/4 - but all pieces have constant meter. For further information see Dixon et al (2004) and Krebs et al (2013).&lt;br /&gt;
Note, we are using the ground truth annotations from Krebs et al. (2013) available at https://github.com/CPJKU/BallroomAnnotations&lt;br /&gt;
&lt;br /&gt;
'''Isophonics (Beatles only)'''&lt;br /&gt;
The Beatles dataset from the Centre for Digital Music at Queen Mary, University of London (http://www.isophonics.net/), as also used for Audio Chord Estimation in MIREX for many years. &lt;br /&gt;
This dataset contains '''179''' complete songs (all except Revolution 9), the majority of which are in 4/4.&lt;br /&gt;
For further information see Mauch et al (2009).&lt;br /&gt;
&lt;br /&gt;
'''Turkish Data'''&lt;br /&gt;
The Turkish corpus is an extended version of the annotated data used in Srinivasamurthy et al. (2014). It includes '''85''' excerpts of one&lt;br /&gt;
minute length each, and each piece belongs to one of four&lt;br /&gt;
rhythm classes that are referred to as usul in Turkish Art&lt;br /&gt;
music. 30 pieces are in the 9/8-usul Aksak, 18 pieces&lt;br /&gt;
in the 10/8-usul Curcuna, 28 samples in the 8/8-usul&lt;br /&gt;
Düyek, and 9 samples in the 4/4 Sofyan.&lt;br /&gt;
&lt;br /&gt;
'''Cretan Data'''&lt;br /&gt;
The corpus of Cretan music consists of '''42''' full length pieces of Cretan leaping dances. While there are several dances that differ in terms of their steps, the differences in&lt;br /&gt;
the sound are most noticeable in the melodic content, and all pieces can be considered to belong to one rhythmic style. All these dances are usually notated using a 2/4 time signature,&lt;br /&gt;
and the accompanying rhythmical patterns are usually played on a Cretan lute. While a variety of rhythmic patterns exist, they do not relate to a specific dance and can be&lt;br /&gt;
assumed to occur in all of the 42 songs in this corpus.&lt;br /&gt;
&lt;br /&gt;
'''Carnatic Data'''&lt;br /&gt;
The Carnatic music dataset is a subset of the CompMusic [http://compmusic.upf.edu/carnatic-rhythm-dataset Carnatic Music Rhythm Dataset]. It includes '''118''' two minute long excerpts spanning four most commonly used tālas (the rhythmic framework of Carnatic music, consisting of time cycles) of Carnatic music. There are 30 examples in each of ādi tāla (8 beats/cycle), rūpaka tāla (3 beats/cycle) and miśra chāpu tāla (7 beats/cycle), and 28 examples in khaṇḍa chāpu tāla (5 beats/cycle). The beats of the tāla in miśra chāpu and khaṇḍa chāpu are non-uniform, but for consistency with other datasets, a uniform beat pulse was obtained by interpolating the non-uniformly spaced beat locations. The recordings consist of both vocal and instrumental music recordings representative of the present day performance practice. All recordings contain percussion accompaniment, mainly the Mridangam. &lt;br /&gt;
&lt;br /&gt;
'''HJDB''' (to be confirmed)&lt;br /&gt;
The HJDB dataset contains '''236''' excerpts of Hardcore, Jungle and Drum and Bass music between 30s and 2 minutes in length. All excerpts are in 4/4 and have a constant tempo. &lt;br /&gt;
For further information see Hockman et al (2012).&lt;br /&gt;
&lt;br /&gt;
In total this makes '''1357''' excerpts (of which 259 are full length songs).&lt;br /&gt;
&lt;br /&gt;
=== Audio Formats ===&lt;br /&gt;
&lt;br /&gt;
The data are monophonic sound files&lt;br /&gt;
&lt;br /&gt;
* CD-quality (PCM, 16-bit, 44100 Hz) for all except Ballroom (originally lower quality, but resampled to 44100 Hz)&lt;br /&gt;
* single channel (mono)&lt;br /&gt;
&lt;br /&gt;
== Submission Format ==&lt;br /&gt;
Submissions to this task will have to conform to a specified format detailed below. Submissions should be packaged and contain at least two files: The algorithm itself and a README containing contact information and detailing, in full, the use of the algorithm.&lt;br /&gt;
&lt;br /&gt;
=== Input Data ===&lt;br /&gt;
Participating algorithms will have to read audio in the following format:&lt;br /&gt;
&lt;br /&gt;
* Sample rate: 44.1 KHz&lt;br /&gt;
* Sample size: 16 bit&lt;br /&gt;
* Number of channels: 1 (mono)&lt;br /&gt;
* Encoding: WAV &lt;br /&gt;
&lt;br /&gt;
=== Output Data ===&lt;br /&gt;
&lt;br /&gt;
The downbeat estimation algorithms will return downbeat times in an ASCII text file for each input .wav audio file. The specification of this output file is immediately below.&lt;br /&gt;
&lt;br /&gt;
=== Output File Format (Audio Downbeat Estimation) ===&lt;br /&gt;
&lt;br /&gt;
The downbeat output file format is an ASCII text format. Each downbeat time is specified, in seconds, on its own line. Specifically, &lt;br /&gt;
&lt;br /&gt;
 &amp;lt;downbeat time (in seconds)&amp;gt;\n&lt;br /&gt;
&lt;br /&gt;
where \n denotes the end of line. The &amp;lt; and &amp;gt; characters are not included. An example output file would look something like:&lt;br /&gt;
&lt;br /&gt;
 0.243&lt;br /&gt;
 1.486&lt;br /&gt;
 2.729&lt;br /&gt;
&lt;br /&gt;
=== Algorithm Calling Format ===&lt;br /&gt;
&lt;br /&gt;
The submitted algorithm must take as arguments a SINGLE .wav file to perform the downbeat estimation as well as the full output path and filename of the output file. The ability to specify the output path and file name is essential. Denoting the input .wav file path and name as %input and the output file path and name as %output, a program called foobar could be called from the command-line as follows:&lt;br /&gt;
&lt;br /&gt;
 foobar %input %output&lt;br /&gt;
 foobar -i %input -o %output&lt;br /&gt;
&lt;br /&gt;
Moreover, if your submission takes additional parameters, such as a detection threshold, foobar could be called like:&lt;br /&gt;
&lt;br /&gt;
 foobar .1 %input %output&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output  &lt;br /&gt;
&lt;br /&gt;
If your submission is in MATLAB, it should be submitted as a function. Once again, the function must contain String inputs for the full path and names of the input and output files. Parameters could also be specified as input arguments of the function. For example: &lt;br /&gt;
&lt;br /&gt;
 foobar('%input','%output')&lt;br /&gt;
 foobar(.1,'%input','%output')&lt;br /&gt;
&lt;br /&gt;
=== README File ===&lt;br /&gt;
&lt;br /&gt;
A README file accompanying each submission should contain explicit instructions on how to to run the program (as well as contact information, etc.). In particular, each command line to run should be specified, using %input for the input sound file and %output for the resulting text file.&lt;br /&gt;
&lt;br /&gt;
For instance, to test the program foobar with different values for parameters param1, the README file would look like:&lt;br /&gt;
&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output&lt;br /&gt;
 foobar -param1 .15 -i %input -o %output&lt;br /&gt;
 foobar -param1 .2 -i %input -o %output&lt;br /&gt;
 foobar -param1 .25 -i %input -o %output&lt;br /&gt;
 foobar -param1 .3 -i %input -o %output&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
For a submission using MATLAB, the README file could look like:&lt;br /&gt;
&lt;br /&gt;
 matlab -r &amp;quot;foobar(.1,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.15,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.2,'%input','%output');quit;&amp;quot; &lt;br /&gt;
 matlab -r &amp;quot;foobar(.25,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.3,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
The different command lines to evaluate the performance of each parameter set over the whole database will be generated automatically from each line in the README file containing both '%input' and '%output' strings.&lt;br /&gt;
&lt;br /&gt;
== Evaluation Procedures ==&lt;br /&gt;
&lt;br /&gt;
For the evalution procedure we will use&lt;br /&gt;
*'''F-measure''' - the standard calculation as used in onset and beat tracking evaluation with a +/-70ms window, see Dixon (2007).&lt;br /&gt;
&lt;br /&gt;
Given the high diversity of musical styles included in the task, results will be reported per each invidiual dataset. &lt;br /&gt;
&lt;br /&gt;
== Example Data ==&lt;br /&gt;
&lt;br /&gt;
A total of 20 beat and downbeat annotated 30s excerpts are available for participants. &lt;br /&gt;
The data (47MB) is available to download here:&lt;br /&gt;
&lt;br /&gt;
https://www.music-ir.org/evaluation/MIREX/data/2014/downbeat/&lt;br /&gt;
&lt;br /&gt;
''User:'' downbeat &lt;br /&gt;
&lt;br /&gt;
''Password:'' d0wn63at&lt;br /&gt;
&lt;br /&gt;
Due to the availability of the Ballroom and Beatles datasets, we only include examples of the remaining styles as follows:&lt;br /&gt;
&lt;br /&gt;
'''HJDB''': 5 excerpts&lt;br /&gt;
&lt;br /&gt;
'''Cretan''': 3 excerpts&lt;br /&gt;
&lt;br /&gt;
'''Carnatic''': 4 excerpts&lt;br /&gt;
&lt;br /&gt;
'''Turkish''': 8 excerpts (2x Aksak, 2x Curcuna, 2x Düyek, and 2x Sofyan).&lt;br /&gt;
&lt;br /&gt;
Please note the audio files are in .flac format.&lt;br /&gt;
&lt;br /&gt;
For each audio file, e.g. ''hjdb1.fla''c there is a corresponding annotation file ''hjdb1.txt'' &lt;br /&gt;
&lt;br /&gt;
This text file contains timestamps corresponding to beat annotations and a label to denote the position in the bar. &lt;br /&gt;
&lt;br /&gt;
All beat times labelled '1' correspond to downbeats. &lt;br /&gt;
&lt;br /&gt;
== Time and hardware limits ==&lt;br /&gt;
Due to the potentially high number of participants in this and other audio tasks, hard limits on the runtime of submissions will be imposed.&lt;br /&gt;
&lt;br /&gt;
A hard limit of 24 hours will be imposed on analysis times. Submissions exceeding this limit may not receive a result.&lt;br /&gt;
&lt;br /&gt;
== Potential Participants ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
Jose R. Zapata / joser.zapata (at) upb.edu.co&lt;br /&gt;
&lt;br /&gt;
== Discussion ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
= Bibliography =&lt;br /&gt;
&lt;br /&gt;
S. Dixon, F. Gouyon and G. Widmer, [http://ismir2004.ismir.net/proceedings/p093-page-509-paper165.pdf Towards Characterisation of Music via Rhythmic Patterns], In Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR 2004), pp 509-516.&lt;br /&gt;
&lt;br /&gt;
S. Dixon, [http://www.eecs.qmul.ac.uk/~simond/pub/2007/jnmr07.pdf Evaluation of audio beat tracking system BeatRoot], Journal of New Music Research, vol. 36, no. 1, pp. 39-51, 2007.&lt;br /&gt;
&lt;br /&gt;
J. A. Hockman, M. E. P. Davies, I. Fujinaga.[http://ismir2012.ismir.net/event/papers/169-ismir-2012.pdf ONE IN THE JUNGLE: Downbeat Detection in Hardcore, Jungle, and Drum and Bass], In Proceedings of 13th International Society for Music Information Retrieval Conference (ISMIR), Porto, Portugal pp. 169-174, 2012.&lt;br /&gt;
&lt;br /&gt;
F. Krebs, S. Boeck, and G. Widmer, [http://www.cp.jku.at/research/papers/Krebs_etal_ISMIR_2013.pdf Rhythmic Pattern Modeling for Beat- and Downbeat Tracking in Musical Audio], In Proceedings of 14th International Society for Music Information Retrieval Conference (ISMIR), Curitiba, Brazil, 2013.&lt;br /&gt;
&lt;br /&gt;
M. Mauch, C. Cannam, M. E. P. Davies, S. Dixon, C. Harte, S. Kolozali and D. Tidhar, [http://ismir2009.ismir.net/proceedings/LBD-18.pdf OMRAS2 Metadata Project 2009], Late-breaking session at the 10th International Conference on Music Information Retrieval, 2009.&lt;br /&gt;
&lt;br /&gt;
A. Srinivasamurthy, A. Holzapfel, and Xavier Serra, [http://www.tandfonline.com/doi/full/10.1080/09298215.2013.879902 In Search of Automatic Rhythm Analysis Methods for Turkish and Indian Art Music], Journal of New Music Research, vol. 43, no. 1, pp. 94-114, 2014.&lt;/div&gt;</summary>
		<author><name>Matthew Davies</name></author>
		
	</entry>
	<entry>
		<id>https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10297</id>
		<title>2014:Audio Downbeat Estimation</title>
		<link rel="alternate" type="text/html" href="https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10297"/>
		<updated>2014-07-22T18:27:08Z</updated>

		<summary type="html">&lt;p&gt;Matthew Davies: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Description ==&lt;br /&gt;
&lt;br /&gt;
'''This task is new for 2014!'''&lt;br /&gt;
&lt;br /&gt;
This text has been adapted from the Audio Beat Tracking Wiki page.  Please add your comments and discussion at the bottom of this page.&lt;br /&gt;
&lt;br /&gt;
The aim of the automatic downbeat estimation task is to identify the locations of downbeats in a collection of sound files. While this is similar to the Audio Beat Tracking task, here the aim is to find the first beat of each bar (measure) rather than all beat times. Algorithms are '''not''' required to estimate beat times or time-signature in addition to downbeats.&lt;br /&gt;
&lt;br /&gt;
Submitted algorithms will be evaluated in terms of their accuracy in finding downbeat locations (only) as annotated by musical experts across several diverse datasets.&lt;br /&gt;
&lt;br /&gt;
'''Update 22/07/14''' A small set of training data is now available. Please see [[#Example_Data]]&lt;br /&gt;
&lt;br /&gt;
== Data ==&lt;br /&gt;
&lt;br /&gt;
=== Collections ===&lt;br /&gt;
'''Ballroom'''&lt;br /&gt;
The ballroom dataset contains eight different dance styles (Cha Cha, Jive, Quickstep, Rumba, Samba, Tango, Viennese Waltz and Waltz). It consists of '''697''' excerpts of 30s in duration.&lt;br /&gt;
The dataset contains two different meters 3/4 and 4/4 - but all pieces have constant meter. For further information see Dixon et al (2004) and Krebs et al (2013).&lt;br /&gt;
Note, we are using the ground truth annotations from Krebs et al. (2013) available at https://github.com/CPJKU/BallroomAnnotations&lt;br /&gt;
&lt;br /&gt;
'''Isophonics (Beatles only)'''&lt;br /&gt;
The Beatles dataset from the Centre for Digital Music at Queen Mary, University of London (http://www.isophonics.net/), as also used for Audio Chord Estimation in MIREX for many years. &lt;br /&gt;
This dataset contains '''179''' complete songs (all except Revolution 9), the majority of which are in 4/4.&lt;br /&gt;
For further information see Mauch et al (2009).&lt;br /&gt;
&lt;br /&gt;
'''Turkish Data'''&lt;br /&gt;
The Turkish corpus is an extended version of the annotated data used in Srinivasamurthy et al. (2014). It includes '''85''' excerpts of one&lt;br /&gt;
minute length each, and each piece belongs to one of four&lt;br /&gt;
rhythm classes that are referred to as usul in Turkish Art&lt;br /&gt;
music. 30 pieces are in the 9/8-usul Aksak, 18 pieces&lt;br /&gt;
in the 10/8-usul Curcuna, 28 samples in the 8/8-usul&lt;br /&gt;
Düyek, and 9 samples in the 4/4 Sofyan.&lt;br /&gt;
&lt;br /&gt;
'''Cretan Data'''&lt;br /&gt;
The corpus of Cretan music consists of '''42''' full length pieces of Cretan leaping dances. While there are several dances that differ in terms of their steps, the differences in&lt;br /&gt;
the sound are most noticeable in the melodic content, and all pieces can be considered to belong to one rhythmic style. All these dances are usually notated using a 2/4 time signature,&lt;br /&gt;
and the accompanying rhythmical patterns are usually played on a Cretan lute. While a variety of rhythmic patterns exist, they do not relate to a specific dance and can be&lt;br /&gt;
assumed to occur in all of the 42 songs in this corpus.&lt;br /&gt;
&lt;br /&gt;
'''Carnatic Data'''&lt;br /&gt;
The Carnatic music dataset is a subset of the CompMusic [http://compmusic.upf.edu/carnatic-rhythm-dataset Carnatic Music Rhythm Dataset]. It includes '''118''' two minute long excerpts spanning four most commonly used tālas (the rhythmic framework of Carnatic music, consisting of time cycles) of Carnatic music. There are 30 examples in each of ādi tāla (8 beats/cycle), rūpaka tāla (3 beats/cycle) and miśra chāpu tāla (7 beats/cycle), and 28 examples in khaṇḍa chāpu tāla (5 beats/cycle). The beats of the tāla in miśra chāpu and khaṇḍa chāpu are non-uniform, but for consistency with other datasets, a uniform beat pulse was obtained by interpolating the non-uniformly spaced beat locations. The recordings consist of both vocal and instrumental music recordings representative of the present day performance practice. All recordings contain percussion accompaniment, mainly the Mridangam. &lt;br /&gt;
&lt;br /&gt;
'''HJDB''' (to be confirmed)&lt;br /&gt;
The HJDB dataset contains '''236''' excerpts of Hardcore, Jungle and Drum and Bass music between 30s and 2 minutes in length. All excerpts are in 4/4 and have a constant tempo. &lt;br /&gt;
For further information see Hockman et al (2012).&lt;br /&gt;
&lt;br /&gt;
In total this makes '''1357''' excerpts (of which 259 are full length songs).&lt;br /&gt;
&lt;br /&gt;
=== Audio Formats ===&lt;br /&gt;
&lt;br /&gt;
The data are monophonic sound files&lt;br /&gt;
&lt;br /&gt;
* CD-quality (PCM, 16-bit, 44100 Hz) for all except Ballroom (originally lower quality, but resampled to 44100 Hz)&lt;br /&gt;
* single channel (mono)&lt;br /&gt;
&lt;br /&gt;
== Submission Format ==&lt;br /&gt;
Submissions to this task will have to conform to a specified format detailed below. Submissions should be packaged and contain at least two files: The algorithm itself and a README containing contact information and detailing, in full, the use of the algorithm.&lt;br /&gt;
&lt;br /&gt;
=== Input Data ===&lt;br /&gt;
Participating algorithms will have to read audio in the following format:&lt;br /&gt;
&lt;br /&gt;
* Sample rate: 44.1 KHz&lt;br /&gt;
* Sample size: 16 bit&lt;br /&gt;
* Number of channels: 1 (mono)&lt;br /&gt;
* Encoding: WAV &lt;br /&gt;
&lt;br /&gt;
=== Output Data ===&lt;br /&gt;
&lt;br /&gt;
The downbeat estimation algorithms will return downbeat times in an ASCII text file for each input .wav audio file. The specification of this output file is immediately below.&lt;br /&gt;
&lt;br /&gt;
=== Output File Format (Audio Downbeat Estimation) ===&lt;br /&gt;
&lt;br /&gt;
The downbeat output file format is an ASCII text format. Each downbeat time is specified, in seconds, on its own line. Specifically, &lt;br /&gt;
&lt;br /&gt;
 &amp;lt;downbeat time (in seconds)&amp;gt;\n&lt;br /&gt;
&lt;br /&gt;
where \n denotes the end of line. The &amp;lt; and &amp;gt; characters are not included. An example output file would look something like:&lt;br /&gt;
&lt;br /&gt;
 0.243&lt;br /&gt;
 1.486&lt;br /&gt;
 2.729&lt;br /&gt;
&lt;br /&gt;
=== Algorithm Calling Format ===&lt;br /&gt;
&lt;br /&gt;
The submitted algorithm must take as arguments a SINGLE .wav file to perform the downbeat estimation as well as the full output path and filename of the output file. The ability to specify the output path and file name is essential. Denoting the input .wav file path and name as %input and the output file path and name as %output, a program called foobar could be called from the command-line as follows:&lt;br /&gt;
&lt;br /&gt;
 foobar %input %output&lt;br /&gt;
 foobar -i %input -o %output&lt;br /&gt;
&lt;br /&gt;
Moreover, if your submission takes additional parameters, such as a detection threshold, foobar could be called like:&lt;br /&gt;
&lt;br /&gt;
 foobar .1 %input %output&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output  &lt;br /&gt;
&lt;br /&gt;
If your submission is in MATLAB, it should be submitted as a function. Once again, the function must contain String inputs for the full path and names of the input and output files. Parameters could also be specified as input arguments of the function. For example: &lt;br /&gt;
&lt;br /&gt;
 foobar('%input','%output')&lt;br /&gt;
 foobar(.1,'%input','%output')&lt;br /&gt;
&lt;br /&gt;
=== README File ===&lt;br /&gt;
&lt;br /&gt;
A README file accompanying each submission should contain explicit instructions on how to to run the program (as well as contact information, etc.). In particular, each command line to run should be specified, using %input for the input sound file and %output for the resulting text file.&lt;br /&gt;
&lt;br /&gt;
For instance, to test the program foobar with different values for parameters param1, the README file would look like:&lt;br /&gt;
&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output&lt;br /&gt;
 foobar -param1 .15 -i %input -o %output&lt;br /&gt;
 foobar -param1 .2 -i %input -o %output&lt;br /&gt;
 foobar -param1 .25 -i %input -o %output&lt;br /&gt;
 foobar -param1 .3 -i %input -o %output&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
For a submission using MATLAB, the README file could look like:&lt;br /&gt;
&lt;br /&gt;
 matlab -r &amp;quot;foobar(.1,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.15,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.2,'%input','%output');quit;&amp;quot; &lt;br /&gt;
 matlab -r &amp;quot;foobar(.25,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.3,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
The different command lines to evaluate the performance of each parameter set over the whole database will be generated automatically from each line in the README file containing both '%input' and '%output' strings.&lt;br /&gt;
&lt;br /&gt;
== Evaluation Procedures ==&lt;br /&gt;
&lt;br /&gt;
For the evalution procedure we will use&lt;br /&gt;
*'''F-measure''' - the standard calculation as used in onset and beat tracking evaluation with a +/-70ms window, see Dixon (2007).&lt;br /&gt;
&lt;br /&gt;
Given the high diversity of musical styles included in the task, results will be reported per each invidiual dataset. &lt;br /&gt;
&lt;br /&gt;
== Example Data ==&lt;br /&gt;
&lt;br /&gt;
A total of 20 beat and downbeat annotated 30s excerpts are available for participants. &lt;br /&gt;
The data (47MB) is available to download here:&lt;br /&gt;
&lt;br /&gt;
https://www.music-ir.org/evaluation/MIREX/data/2014/downbeat/&lt;br /&gt;
&lt;br /&gt;
''User:'' downbeat &lt;br /&gt;
&lt;br /&gt;
''Password:'' d0wn63at&lt;br /&gt;
&lt;br /&gt;
Due to the availability of the Ballroom and Beatles datasets, we only include examples of the remaining styles as follows:&lt;br /&gt;
&lt;br /&gt;
'''HJDB''': 5 excerpts&lt;br /&gt;
&lt;br /&gt;
'''Cretan''': 3 excerpts&lt;br /&gt;
&lt;br /&gt;
'''Carnatic''': 4 excerpts&lt;br /&gt;
&lt;br /&gt;
'''Turkish''': 8 excerpts (2x Aksak, 2x Curcuna, 2x Düyek, and 2x Sofyan).&lt;br /&gt;
&lt;br /&gt;
Please note the audio files are in .flac format&lt;br /&gt;
&lt;br /&gt;
== Time and hardware limits ==&lt;br /&gt;
Due to the potentially high number of participants in this and other audio tasks, hard limits on the runtime of submissions will be imposed.&lt;br /&gt;
&lt;br /&gt;
A hard limit of 24 hours will be imposed on analysis times. Submissions exceeding this limit may not receive a result.&lt;br /&gt;
&lt;br /&gt;
== Potential Participants ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
Jose R. Zapata / joser.zapata (at) upb.edu.co&lt;br /&gt;
&lt;br /&gt;
== Discussion ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
= Bibliography =&lt;br /&gt;
&lt;br /&gt;
S. Dixon, F. Gouyon and G. Widmer, [http://ismir2004.ismir.net/proceedings/p093-page-509-paper165.pdf Towards Characterisation of Music via Rhythmic Patterns], In Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR 2004), pp 509-516.&lt;br /&gt;
&lt;br /&gt;
S. Dixon, [http://www.eecs.qmul.ac.uk/~simond/pub/2007/jnmr07.pdf Evaluation of audio beat tracking system BeatRoot], Journal of New Music Research, vol. 36, no. 1, pp. 39-51, 2007.&lt;br /&gt;
&lt;br /&gt;
J. A. Hockman, M. E. P. Davies, I. Fujinaga.[http://ismir2012.ismir.net/event/papers/169-ismir-2012.pdf ONE IN THE JUNGLE: Downbeat Detection in Hardcore, Jungle, and Drum and Bass], In Proceedings of 13th International Society for Music Information Retrieval Conference (ISMIR), Porto, Portugal pp. 169-174, 2012.&lt;br /&gt;
&lt;br /&gt;
F. Krebs, S. Boeck, and G. Widmer, [http://www.cp.jku.at/research/papers/Krebs_etal_ISMIR_2013.pdf Rhythmic Pattern Modeling for Beat- and Downbeat Tracking in Musical Audio], In Proceedings of 14th International Society for Music Information Retrieval Conference (ISMIR), Curitiba, Brazil, 2013.&lt;br /&gt;
&lt;br /&gt;
M. Mauch, C. Cannam, M. E. P. Davies, S. Dixon, C. Harte, S. Kolozali and D. Tidhar, [http://ismir2009.ismir.net/proceedings/LBD-18.pdf OMRAS2 Metadata Project 2009], Late-breaking session at the 10th International Conference on Music Information Retrieval, 2009.&lt;br /&gt;
&lt;br /&gt;
A. Srinivasamurthy, A. Holzapfel, and Xavier Serra, [http://www.tandfonline.com/doi/full/10.1080/09298215.2013.879902 In Search of Automatic Rhythm Analysis Methods for Turkish and Indian Art Music], Journal of New Music Research, vol. 43, no. 1, pp. 94-114, 2014.&lt;/div&gt;</summary>
		<author><name>Matthew Davies</name></author>
		
	</entry>
	<entry>
		<id>https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10296</id>
		<title>2014:Audio Downbeat Estimation</title>
		<link rel="alternate" type="text/html" href="https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10296"/>
		<updated>2014-07-22T18:26:33Z</updated>

		<summary type="html">&lt;p&gt;Matthew Davies: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Description ==&lt;br /&gt;
&lt;br /&gt;
'''This task is new for 2014!'''&lt;br /&gt;
&lt;br /&gt;
This text has been adapted from the Audio Beat Tracking Wiki page.  Please add your comments and discussion at the bottom of this page.&lt;br /&gt;
&lt;br /&gt;
The aim of the automatic downbeat estimation task is to identify the locations of downbeats in a collection of sound files. While this is similar to the Audio Beat Tracking task, here the aim is to find the first beat of each bar (measure) rather than all beat times. Algorithms are '''not''' required to estimate beat times or time-signature in addition to downbeats.&lt;br /&gt;
&lt;br /&gt;
Submitted algorithms will be evaluated in terms of their accuracy in finding downbeat locations (only) as annotated by musical experts across several diverse datasets.&lt;br /&gt;
&lt;br /&gt;
'''Update 22/07/14''' A small set of training data is now available. Please see [[#Example_Data]]&lt;br /&gt;
&lt;br /&gt;
== Data ==&lt;br /&gt;
&lt;br /&gt;
=== Collections ===&lt;br /&gt;
'''Ballroom'''&lt;br /&gt;
The ballroom dataset contains eight different dance styles (Cha Cha, Jive, Quickstep, Rumba, Samba, Tango, Viennese Waltz and Waltz). It consists of '''697''' excerpts of 30s in duration.&lt;br /&gt;
The dataset contains two different meters 3/4 and 4/4 - but all pieces have constant meter. For further information see Dixon et al (2004) and Krebs et al (2013).&lt;br /&gt;
Note, we are using the ground truth annotations from Krebs et al. (2013) available at https://github.com/CPJKU/BallroomAnnotations&lt;br /&gt;
&lt;br /&gt;
'''Isophonics (Beatles only)'''&lt;br /&gt;
The Beatles dataset from the Centre for Digital Music at Queen Mary, University of London (http://www.isophonics.net/), as also used for Audio Chord Estimation in MIREX for many years. &lt;br /&gt;
This dataset contains '''179''' complete songs (all except Revolution 9), the majority of which are in 4/4.&lt;br /&gt;
For further information see Mauch et al (2009).&lt;br /&gt;
&lt;br /&gt;
'''Turkish Data'''&lt;br /&gt;
The Turkish corpus is an extended version of the annotated data used in Srinivasamurthy et al. (2014). It includes '''85''' excerpts of one&lt;br /&gt;
minute length each, and each piece belongs to one of four&lt;br /&gt;
rhythm classes that are referred to as usul in Turkish Art&lt;br /&gt;
music. 30 pieces are in the 9/8-usul Aksak, 18 pieces&lt;br /&gt;
in the 10/8-usul Curcuna, 28 samples in the 8/8-usul&lt;br /&gt;
Düyek, and 9 samples in the 4/4 Sofyan.&lt;br /&gt;
&lt;br /&gt;
'''Cretan Data'''&lt;br /&gt;
The corpus of Cretan music consists of '''42''' full length pieces of Cretan leaping dances. While there are several dances that differ in terms of their steps, the differences in&lt;br /&gt;
the sound are most noticeable in the melodic content, and all pieces can be considered to belong to one rhythmic style. All these dances are usually notated using a 2/4 time signature,&lt;br /&gt;
and the accompanying rhythmical patterns are usually played on a Cretan lute. While a variety of rhythmic patterns exist, they do not relate to a specific dance and can be&lt;br /&gt;
assumed to occur in all of the 42 songs in this corpus.&lt;br /&gt;
&lt;br /&gt;
'''Carnatic Data'''&lt;br /&gt;
The Carnatic music dataset is a subset of the CompMusic [http://compmusic.upf.edu/carnatic-rhythm-dataset Carnatic Music Rhythm Dataset]. It includes '''118''' two minute long excerpts spanning four most commonly used tālas (the rhythmic framework of Carnatic music, consisting of time cycles) of Carnatic music. There are 30 examples in each of ādi tāla (8 beats/cycle), rūpaka tāla (3 beats/cycle) and miśra chāpu tāla (7 beats/cycle), and 28 examples in khaṇḍa chāpu tāla (5 beats/cycle). The beats of the tāla in miśra chāpu and khaṇḍa chāpu are non-uniform, but for consistency with other datasets, a uniform beat pulse was obtained by interpolating the non-uniformly spaced beat locations. The recordings consist of both vocal and instrumental music recordings representative of the present day performance practice. All recordings contain percussion accompaniment, mainly the Mridangam. &lt;br /&gt;
&lt;br /&gt;
'''HJDB''' (to be confirmed)&lt;br /&gt;
The HJDB dataset contains '''236''' excerpts of Hardcore, Jungle and Drum and Bass music between 30s and 2 minutes in length. All excerpts are in 4/4 and have a constant tempo. &lt;br /&gt;
For further information see Hockman et al (2012).&lt;br /&gt;
&lt;br /&gt;
In total this makes '''1357''' excerpts (of which 259 are full length songs).&lt;br /&gt;
&lt;br /&gt;
=== Audio Formats ===&lt;br /&gt;
&lt;br /&gt;
The data are monophonic sound files&lt;br /&gt;
&lt;br /&gt;
* CD-quality (PCM, 16-bit, 44100 Hz) for all except Ballroom (originally lower quality, but resampled to 44100 Hz)&lt;br /&gt;
* single channel (mono)&lt;br /&gt;
&lt;br /&gt;
== Submission Format ==&lt;br /&gt;
Submissions to this task will have to conform to a specified format detailed below. Submissions should be packaged and contain at least two files: The algorithm itself and a README containing contact information and detailing, in full, the use of the algorithm.&lt;br /&gt;
&lt;br /&gt;
=== Input Data ===&lt;br /&gt;
Participating algorithms will have to read audio in the following format:&lt;br /&gt;
&lt;br /&gt;
* Sample rate: 44.1 KHz&lt;br /&gt;
* Sample size: 16 bit&lt;br /&gt;
* Number of channels: 1 (mono)&lt;br /&gt;
* Encoding: WAV &lt;br /&gt;
&lt;br /&gt;
=== Output Data ===&lt;br /&gt;
&lt;br /&gt;
The downbeat estimation algorithms will return downbeat times in an ASCII text file for each input .wav audio file. The specification of this output file is immediately below.&lt;br /&gt;
&lt;br /&gt;
=== Output File Format (Audio Downbeat Estimation) ===&lt;br /&gt;
&lt;br /&gt;
The downbeat output file format is an ASCII text format. Each downbeat time is specified, in seconds, on its own line. Specifically, &lt;br /&gt;
&lt;br /&gt;
 &amp;lt;downbeat time (in seconds)&amp;gt;\n&lt;br /&gt;
&lt;br /&gt;
where \n denotes the end of line. The &amp;lt; and &amp;gt; characters are not included. An example output file would look something like:&lt;br /&gt;
&lt;br /&gt;
 0.243&lt;br /&gt;
 1.486&lt;br /&gt;
 2.729&lt;br /&gt;
&lt;br /&gt;
=== Algorithm Calling Format ===&lt;br /&gt;
&lt;br /&gt;
The submitted algorithm must take as arguments a SINGLE .wav file to perform the downbeat estimation as well as the full output path and filename of the output file. The ability to specify the output path and file name is essential. Denoting the input .wav file path and name as %input and the output file path and name as %output, a program called foobar could be called from the command-line as follows:&lt;br /&gt;
&lt;br /&gt;
 foobar %input %output&lt;br /&gt;
 foobar -i %input -o %output&lt;br /&gt;
&lt;br /&gt;
Moreover, if your submission takes additional parameters, such as a detection threshold, foobar could be called like:&lt;br /&gt;
&lt;br /&gt;
 foobar .1 %input %output&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output  &lt;br /&gt;
&lt;br /&gt;
If your submission is in MATLAB, it should be submitted as a function. Once again, the function must contain String inputs for the full path and names of the input and output files. Parameters could also be specified as input arguments of the function. For example: &lt;br /&gt;
&lt;br /&gt;
 foobar('%input','%output')&lt;br /&gt;
 foobar(.1,'%input','%output')&lt;br /&gt;
&lt;br /&gt;
=== README File ===&lt;br /&gt;
&lt;br /&gt;
A README file accompanying each submission should contain explicit instructions on how to to run the program (as well as contact information, etc.). In particular, each command line to run should be specified, using %input for the input sound file and %output for the resulting text file.&lt;br /&gt;
&lt;br /&gt;
For instance, to test the program foobar with different values for parameters param1, the README file would look like:&lt;br /&gt;
&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output&lt;br /&gt;
 foobar -param1 .15 -i %input -o %output&lt;br /&gt;
 foobar -param1 .2 -i %input -o %output&lt;br /&gt;
 foobar -param1 .25 -i %input -o %output&lt;br /&gt;
 foobar -param1 .3 -i %input -o %output&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
For a submission using MATLAB, the README file could look like:&lt;br /&gt;
&lt;br /&gt;
 matlab -r &amp;quot;foobar(.1,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.15,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.2,'%input','%output');quit;&amp;quot; &lt;br /&gt;
 matlab -r &amp;quot;foobar(.25,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.3,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
The different command lines to evaluate the performance of each parameter set over the whole database will be generated automatically from each line in the README file containing both '%input' and '%output' strings.&lt;br /&gt;
&lt;br /&gt;
== Evaluation Procedures ==&lt;br /&gt;
&lt;br /&gt;
For the evalution procedure we will use&lt;br /&gt;
*'''F-measure''' - the standard calculation as used in onset and beat tracking evaluation with a +/-70ms window, see Dixon (2007).&lt;br /&gt;
&lt;br /&gt;
Given the high diversity of musical styles included in the task, results will be reported per each invidiual dataset. &lt;br /&gt;
&lt;br /&gt;
== Example Data ==&lt;br /&gt;
&lt;br /&gt;
A total of 20 beat and downbeat annotated 30s excerpts are available for participants. &lt;br /&gt;
The data is available to download here:&lt;br /&gt;
&lt;br /&gt;
https://www.music-ir.org/evaluation/MIREX/data/2014/downbeat/&lt;br /&gt;
&lt;br /&gt;
''User:'' downbeat &lt;br /&gt;
&lt;br /&gt;
''Password:'' d0wn63at&lt;br /&gt;
&lt;br /&gt;
Due to the availability of the Ballroom and Beatles datasets, we only include examples of the remaining styles as follows:&lt;br /&gt;
&lt;br /&gt;
'''HJDB''': 5 excerpts&lt;br /&gt;
&lt;br /&gt;
'''Cretan''': 3 excerpts&lt;br /&gt;
&lt;br /&gt;
'''Carnatic''': 4 excerpts&lt;br /&gt;
&lt;br /&gt;
'''Turkish''': 8 excerpts (2x Aksak, 2x Curcuna, 2x Düyek, and 2x Sofyan).&lt;br /&gt;
&lt;br /&gt;
Please note the audio files are in .flac format&lt;br /&gt;
&lt;br /&gt;
== Time and hardware limits ==&lt;br /&gt;
Due to the potentially high number of participants in this and other audio tasks, hard limits on the runtime of submissions will be imposed.&lt;br /&gt;
&lt;br /&gt;
A hard limit of 24 hours will be imposed on analysis times. Submissions exceeding this limit may not receive a result.&lt;br /&gt;
&lt;br /&gt;
== Potential Participants ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
Jose R. Zapata / joser.zapata (at) upb.edu.co&lt;br /&gt;
&lt;br /&gt;
== Discussion ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
= Bibliography =&lt;br /&gt;
&lt;br /&gt;
S. Dixon, F. Gouyon and G. Widmer, [http://ismir2004.ismir.net/proceedings/p093-page-509-paper165.pdf Towards Characterisation of Music via Rhythmic Patterns], In Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR 2004), pp 509-516.&lt;br /&gt;
&lt;br /&gt;
S. Dixon, [http://www.eecs.qmul.ac.uk/~simond/pub/2007/jnmr07.pdf Evaluation of audio beat tracking system BeatRoot], Journal of New Music Research, vol. 36, no. 1, pp. 39-51, 2007.&lt;br /&gt;
&lt;br /&gt;
J. A. Hockman, M. E. P. Davies, I. Fujinaga.[http://ismir2012.ismir.net/event/papers/169-ismir-2012.pdf ONE IN THE JUNGLE: Downbeat Detection in Hardcore, Jungle, and Drum and Bass], In Proceedings of 13th International Society for Music Information Retrieval Conference (ISMIR), Porto, Portugal pp. 169-174, 2012.&lt;br /&gt;
&lt;br /&gt;
F. Krebs, S. Boeck, and G. Widmer, [http://www.cp.jku.at/research/papers/Krebs_etal_ISMIR_2013.pdf Rhythmic Pattern Modeling for Beat- and Downbeat Tracking in Musical Audio], In Proceedings of 14th International Society for Music Information Retrieval Conference (ISMIR), Curitiba, Brazil, 2013.&lt;br /&gt;
&lt;br /&gt;
M. Mauch, C. Cannam, M. E. P. Davies, S. Dixon, C. Harte, S. Kolozali and D. Tidhar, [http://ismir2009.ismir.net/proceedings/LBD-18.pdf OMRAS2 Metadata Project 2009], Late-breaking session at the 10th International Conference on Music Information Retrieval, 2009.&lt;br /&gt;
&lt;br /&gt;
A. Srinivasamurthy, A. Holzapfel, and Xavier Serra, [http://www.tandfonline.com/doi/full/10.1080/09298215.2013.879902 In Search of Automatic Rhythm Analysis Methods for Turkish and Indian Art Music], Journal of New Music Research, vol. 43, no. 1, pp. 94-114, 2014.&lt;/div&gt;</summary>
		<author><name>Matthew Davies</name></author>
		
	</entry>
	<entry>
		<id>https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10295</id>
		<title>2014:Audio Downbeat Estimation</title>
		<link rel="alternate" type="text/html" href="https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10295"/>
		<updated>2014-07-22T18:25:48Z</updated>

		<summary type="html">&lt;p&gt;Matthew Davies: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Description ==&lt;br /&gt;
&lt;br /&gt;
'''This task is new for 2014!'''&lt;br /&gt;
&lt;br /&gt;
This text has been adapted from the Audio Beat Tracking Wiki page.  Please add your comments and discussion at the bottom of this page.&lt;br /&gt;
&lt;br /&gt;
The aim of the automatic downbeat estimation task is to identify the locations of downbeats in a collection of sound files. While this is similar to the Audio Beat Tracking task, here the aim is to find the first beat of each bar (measure) rather than all beat times. Algorithms are '''not''' required to estimate beat times or time-signature in addition to downbeats.&lt;br /&gt;
&lt;br /&gt;
Submitted algorithms will be evaluated in terms of their accuracy in finding downbeat locations (only) as annotated by musical experts across several diverse datasets.&lt;br /&gt;
&lt;br /&gt;
'''Update 22/07/14''' A small set of training data is now available. Please see [[#Example_Data]]&lt;br /&gt;
&lt;br /&gt;
== Data ==&lt;br /&gt;
&lt;br /&gt;
=== Collections ===&lt;br /&gt;
'''Ballroom'''&lt;br /&gt;
The ballroom dataset contains eight different dance styles (Cha Cha, Jive, Quickstep, Rumba, Samba, Tango, Viennese Waltz and Waltz). It consists of '''697''' excerpts of 30s in duration.&lt;br /&gt;
The dataset contains two different meters 3/4 and 4/4 - but all pieces have constant meter. For further information see Dixon et al (2004) and Krebs et al (2013).&lt;br /&gt;
Note, we are using the ground truth annotations from Krebs et al. (2013) available at https://github.com/CPJKU/BallroomAnnotations&lt;br /&gt;
&lt;br /&gt;
'''Isophonics (Beatles only)'''&lt;br /&gt;
The Beatles dataset from the Centre for Digital Music at Queen Mary, University of London (http://www.isophonics.net/), as also used for Audio Chord Estimation in MIREX for many years. &lt;br /&gt;
This dataset contains '''179''' complete songs (all except Revolution 9), the majority of which are in 4/4.&lt;br /&gt;
For further information see Mauch et al (2009).&lt;br /&gt;
&lt;br /&gt;
'''Turkish Data'''&lt;br /&gt;
The Turkish corpus is an extended version of the annotated data used in Srinivasamurthy et al. (2014). It includes '''85''' excerpts of one&lt;br /&gt;
minute length each, and each piece belongs to one of four&lt;br /&gt;
rhythm classes that are referred to as usul in Turkish Art&lt;br /&gt;
music. 30 pieces are in the 9/8-usul Aksak, 18 pieces&lt;br /&gt;
in the 10/8-usul Curcuna, 28 samples in the 8/8-usul&lt;br /&gt;
Düyek, and 9 samples in the 4/4 Sofyan.&lt;br /&gt;
&lt;br /&gt;
'''Cretan Data'''&lt;br /&gt;
The corpus of Cretan music consists of '''42''' full length pieces of Cretan leaping dances. While there are several dances that differ in terms of their steps, the differences in&lt;br /&gt;
the sound are most noticeable in the melodic content, and all pieces can be considered to belong to one rhythmic style. All these dances are usually notated using a 2/4 time signature,&lt;br /&gt;
and the accompanying rhythmical patterns are usually played on a Cretan lute. While a variety of rhythmic patterns exist, they do not relate to a specific dance and can be&lt;br /&gt;
assumed to occur in all of the 42 songs in this corpus.&lt;br /&gt;
&lt;br /&gt;
'''Carnatic Data'''&lt;br /&gt;
The Carnatic music dataset is a subset of the CompMusic [http://compmusic.upf.edu/carnatic-rhythm-dataset Carnatic Music Rhythm Dataset]. It includes '''118''' two minute long excerpts spanning four most commonly used tālas (the rhythmic framework of Carnatic music, consisting of time cycles) of Carnatic music. There are 30 examples in each of ādi tāla (8 beats/cycle), rūpaka tāla (3 beats/cycle) and miśra chāpu tāla (7 beats/cycle), and 28 examples in khaṇḍa chāpu tāla (5 beats/cycle). The beats of the tāla in miśra chāpu and khaṇḍa chāpu are non-uniform, but for consistency with other datasets, a uniform beat pulse was obtained by interpolating the non-uniformly spaced beat locations. The recordings consist of both vocal and instrumental music recordings representative of the present day performance practice. All recordings contain percussion accompaniment, mainly the Mridangam. &lt;br /&gt;
&lt;br /&gt;
'''HJDB''' (to be confirmed)&lt;br /&gt;
The HJDB dataset contains '''236''' excerpts of Hardcore, Jungle and Drum and Bass music between 30s and 2 minutes in length. All excerpts are in 4/4 and have a constant tempo. &lt;br /&gt;
For further information see Hockman et al (2012).&lt;br /&gt;
&lt;br /&gt;
In total this makes '''1357''' excerpts (of which 259 are full length songs).&lt;br /&gt;
&lt;br /&gt;
=== Audio Formats ===&lt;br /&gt;
&lt;br /&gt;
The data are monophonic sound files&lt;br /&gt;
&lt;br /&gt;
* CD-quality (PCM, 16-bit, 44100 Hz) for all except Ballroom (originally lower quality, but resampled to 44100 Hz)&lt;br /&gt;
* single channel (mono)&lt;br /&gt;
&lt;br /&gt;
== Submission Format ==&lt;br /&gt;
Submissions to this task will have to conform to a specified format detailed below. Submissions should be packaged and contain at least two files: The algorithm itself and a README containing contact information and detailing, in full, the use of the algorithm.&lt;br /&gt;
&lt;br /&gt;
=== Input Data ===&lt;br /&gt;
Participating algorithms will have to read audio in the following format:&lt;br /&gt;
&lt;br /&gt;
* Sample rate: 44.1 KHz&lt;br /&gt;
* Sample size: 16 bit&lt;br /&gt;
* Number of channels: 1 (mono)&lt;br /&gt;
* Encoding: WAV &lt;br /&gt;
&lt;br /&gt;
=== Output Data ===&lt;br /&gt;
&lt;br /&gt;
The downbeat estimation algorithms will return downbeat times in an ASCII text file for each input .wav audio file. The specification of this output file is immediately below.&lt;br /&gt;
&lt;br /&gt;
=== Output File Format (Audio Downbeat Estimation) ===&lt;br /&gt;
&lt;br /&gt;
The downbeat output file format is an ASCII text format. Each downbeat time is specified, in seconds, on its own line. Specifically, &lt;br /&gt;
&lt;br /&gt;
 &amp;lt;downbeat time (in seconds)&amp;gt;\n&lt;br /&gt;
&lt;br /&gt;
where \n denotes the end of line. The &amp;lt; and &amp;gt; characters are not included. An example output file would look something like:&lt;br /&gt;
&lt;br /&gt;
 0.243&lt;br /&gt;
 1.486&lt;br /&gt;
 2.729&lt;br /&gt;
&lt;br /&gt;
=== Algorithm Calling Format ===&lt;br /&gt;
&lt;br /&gt;
The submitted algorithm must take as arguments a SINGLE .wav file to perform the downbeat estimation as well as the full output path and filename of the output file. The ability to specify the output path and file name is essential. Denoting the input .wav file path and name as %input and the output file path and name as %output, a program called foobar could be called from the command-line as follows:&lt;br /&gt;
&lt;br /&gt;
 foobar %input %output&lt;br /&gt;
 foobar -i %input -o %output&lt;br /&gt;
&lt;br /&gt;
Moreover, if your submission takes additional parameters, such as a detection threshold, foobar could be called like:&lt;br /&gt;
&lt;br /&gt;
 foobar .1 %input %output&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output  &lt;br /&gt;
&lt;br /&gt;
If your submission is in MATLAB, it should be submitted as a function. Once again, the function must contain String inputs for the full path and names of the input and output files. Parameters could also be specified as input arguments of the function. For example: &lt;br /&gt;
&lt;br /&gt;
 foobar('%input','%output')&lt;br /&gt;
 foobar(.1,'%input','%output')&lt;br /&gt;
&lt;br /&gt;
=== README File ===&lt;br /&gt;
&lt;br /&gt;
A README file accompanying each submission should contain explicit instructions on how to to run the program (as well as contact information, etc.). In particular, each command line to run should be specified, using %input for the input sound file and %output for the resulting text file.&lt;br /&gt;
&lt;br /&gt;
For instance, to test the program foobar with different values for parameters param1, the README file would look like:&lt;br /&gt;
&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output&lt;br /&gt;
 foobar -param1 .15 -i %input -o %output&lt;br /&gt;
 foobar -param1 .2 -i %input -o %output&lt;br /&gt;
 foobar -param1 .25 -i %input -o %output&lt;br /&gt;
 foobar -param1 .3 -i %input -o %output&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
For a submission using MATLAB, the README file could look like:&lt;br /&gt;
&lt;br /&gt;
 matlab -r &amp;quot;foobar(.1,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.15,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.2,'%input','%output');quit;&amp;quot; &lt;br /&gt;
 matlab -r &amp;quot;foobar(.25,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.3,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
The different command lines to evaluate the performance of each parameter set over the whole database will be generated automatically from each line in the README file containing both '%input' and '%output' strings.&lt;br /&gt;
&lt;br /&gt;
== Evaluation Procedures ==&lt;br /&gt;
&lt;br /&gt;
For the evalution procedure we will use&lt;br /&gt;
*'''F-measure''' - the standard calculation as used in onset and beat tracking evaluation with a +/-70ms window, see Dixon (2007).&lt;br /&gt;
&lt;br /&gt;
Given the high diversity of musical styles included in the task, results will be reported per each invidiual dataset. &lt;br /&gt;
&lt;br /&gt;
== Example Data ==&lt;br /&gt;
&lt;br /&gt;
A total of 20 beat and downbeat annotated 30s excerpts are available for participants. &lt;br /&gt;
The data is available to download here:&lt;br /&gt;
&lt;br /&gt;
https://www.music-ir.org/evaluation/MIREX/data/2014/downbeat/&lt;br /&gt;
&lt;br /&gt;
''User'': downbeat &lt;br /&gt;
&lt;br /&gt;
''Password'': d0wn63at&lt;br /&gt;
&lt;br /&gt;
Due to the availability of the Ballroom and Beatles datasets, we only include examples of the remaining styles as follows:&lt;br /&gt;
&lt;br /&gt;
'''HJDB''': 5 excerpts&lt;br /&gt;
&lt;br /&gt;
'''Cretan''': 3 excerpts&lt;br /&gt;
&lt;br /&gt;
'''Carnatic''': 4 excerpts&lt;br /&gt;
&lt;br /&gt;
'''Turkish''': 8 excerpts (2x Aksak, 2x Curcuna, 2x Düyek, and 2x Sofyan).&lt;br /&gt;
&lt;br /&gt;
Please note the audio files are in .flac format&lt;br /&gt;
&lt;br /&gt;
== Time and hardware limits ==&lt;br /&gt;
Due to the potentially high number of participants in this and other audio tasks, hard limits on the runtime of submissions will be imposed.&lt;br /&gt;
&lt;br /&gt;
A hard limit of 24 hours will be imposed on analysis times. Submissions exceeding this limit may not receive a result.&lt;br /&gt;
&lt;br /&gt;
== Potential Participants ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
Jose R. Zapata / joser.zapata (at) upb.edu.co&lt;br /&gt;
&lt;br /&gt;
== Discussion ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
= Bibliography =&lt;br /&gt;
&lt;br /&gt;
S. Dixon, F. Gouyon and G. Widmer, [http://ismir2004.ismir.net/proceedings/p093-page-509-paper165.pdf Towards Characterisation of Music via Rhythmic Patterns], In Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR 2004), pp 509-516.&lt;br /&gt;
&lt;br /&gt;
S. Dixon, [http://www.eecs.qmul.ac.uk/~simond/pub/2007/jnmr07.pdf Evaluation of audio beat tracking system BeatRoot], Journal of New Music Research, vol. 36, no. 1, pp. 39-51, 2007.&lt;br /&gt;
&lt;br /&gt;
J. A. Hockman, M. E. P. Davies, I. Fujinaga.[http://ismir2012.ismir.net/event/papers/169-ismir-2012.pdf ONE IN THE JUNGLE: Downbeat Detection in Hardcore, Jungle, and Drum and Bass], In Proceedings of 13th International Society for Music Information Retrieval Conference (ISMIR), Porto, Portugal pp. 169-174, 2012.&lt;br /&gt;
&lt;br /&gt;
F. Krebs, S. Boeck, and G. Widmer, [http://www.cp.jku.at/research/papers/Krebs_etal_ISMIR_2013.pdf Rhythmic Pattern Modeling for Beat- and Downbeat Tracking in Musical Audio], In Proceedings of 14th International Society for Music Information Retrieval Conference (ISMIR), Curitiba, Brazil, 2013.&lt;br /&gt;
&lt;br /&gt;
M. Mauch, C. Cannam, M. E. P. Davies, S. Dixon, C. Harte, S. Kolozali and D. Tidhar, [http://ismir2009.ismir.net/proceedings/LBD-18.pdf OMRAS2 Metadata Project 2009], Late-breaking session at the 10th International Conference on Music Information Retrieval, 2009.&lt;br /&gt;
&lt;br /&gt;
A. Srinivasamurthy, A. Holzapfel, and Xavier Serra, [http://www.tandfonline.com/doi/full/10.1080/09298215.2013.879902 In Search of Automatic Rhythm Analysis Methods for Turkish and Indian Art Music], Journal of New Music Research, vol. 43, no. 1, pp. 94-114, 2014.&lt;/div&gt;</summary>
		<author><name>Matthew Davies</name></author>
		
	</entry>
	<entry>
		<id>https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10294</id>
		<title>2014:Audio Downbeat Estimation</title>
		<link rel="alternate" type="text/html" href="https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10294"/>
		<updated>2014-07-22T18:24:57Z</updated>

		<summary type="html">&lt;p&gt;Matthew Davies: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Description ==&lt;br /&gt;
&lt;br /&gt;
'''This task is new for 2014!'''&lt;br /&gt;
&lt;br /&gt;
This text has been adapted from the Audio Beat Tracking Wiki page.  Please add your comments and discussion at the bottom of this page.&lt;br /&gt;
&lt;br /&gt;
The aim of the automatic downbeat estimation task is to identify the locations of downbeats in a collection of sound files. While this is similar to the Audio Beat Tracking task, here the aim is to find the first beat of each bar (measure) rather than all beat times. Algorithms are '''not''' required to estimate beat times or time-signature in addition to downbeats.&lt;br /&gt;
&lt;br /&gt;
Submitted algorithms will be evaluated in terms of their accuracy in finding downbeat locations (only) as annotated by musical experts across several diverse datasets.&lt;br /&gt;
&lt;br /&gt;
'''Update 22/07/14''' A small set of training data is now available. Please see [[#Example_Data]]&lt;br /&gt;
&lt;br /&gt;
== Data ==&lt;br /&gt;
&lt;br /&gt;
=== Collections ===&lt;br /&gt;
'''Ballroom'''&lt;br /&gt;
The ballroom dataset contains eight different dance styles (Cha Cha, Jive, Quickstep, Rumba, Samba, Tango, Viennese Waltz and Waltz). It consists of '''697''' excerpts of 30s in duration.&lt;br /&gt;
The dataset contains two different meters 3/4 and 4/4 - but all pieces have constant meter. For further information see Dixon et al (2004) and Krebs et al (2013).&lt;br /&gt;
Note, we are using the ground truth annotations from Krebs et al. (2013) available at https://github.com/CPJKU/BallroomAnnotations&lt;br /&gt;
&lt;br /&gt;
'''Isophonics (Beatles only)'''&lt;br /&gt;
The Beatles dataset from the Centre for Digital Music at Queen Mary, University of London (http://www.isophonics.net/), as also used for Audio Chord Estimation in MIREX for many years. &lt;br /&gt;
This dataset contains '''179''' complete songs (all except Revolution 9), the majority of which are in 4/4.&lt;br /&gt;
For further information see Mauch et al (2009).&lt;br /&gt;
&lt;br /&gt;
'''Turkish Data'''&lt;br /&gt;
The Turkish corpus is an extended version of the annotated data used in Srinivasamurthy et al. (2014). It includes '''85''' excerpts of one&lt;br /&gt;
minute length each, and each piece belongs to one of four&lt;br /&gt;
rhythm classes that are referred to as usul in Turkish Art&lt;br /&gt;
music. 30 pieces are in the 9/8-usul Aksak, 18 pieces&lt;br /&gt;
in the 10/8-usul Curcuna, 28 samples in the 8/8-usul&lt;br /&gt;
Düyek, and 9 samples in the 4/4 Sofyan.&lt;br /&gt;
&lt;br /&gt;
'''Cretan Data'''&lt;br /&gt;
The corpus of Cretan music consists of '''42''' full length pieces of Cretan leaping dances. While there are several dances that differ in terms of their steps, the differences in&lt;br /&gt;
the sound are most noticeable in the melodic content, and all pieces can be considered to belong to one rhythmic style. All these dances are usually notated using a 2/4 time signature,&lt;br /&gt;
and the accompanying rhythmical patterns are usually played on a Cretan lute. While a variety of rhythmic patterns exist, they do not relate to a specific dance and can be&lt;br /&gt;
assumed to occur in all of the 42 songs in this corpus.&lt;br /&gt;
&lt;br /&gt;
'''Carnatic Data'''&lt;br /&gt;
The Carnatic music dataset is a subset of the CompMusic [http://compmusic.upf.edu/carnatic-rhythm-dataset Carnatic Music Rhythm Dataset]. It includes '''118''' two minute long excerpts spanning four most commonly used tālas (the rhythmic framework of Carnatic music, consisting of time cycles) of Carnatic music. There are 30 examples in each of ādi tāla (8 beats/cycle), rūpaka tāla (3 beats/cycle) and miśra chāpu tāla (7 beats/cycle), and 28 examples in khaṇḍa chāpu tāla (5 beats/cycle). The beats of the tāla in miśra chāpu and khaṇḍa chāpu are non-uniform, but for consistency with other datasets, a uniform beat pulse was obtained by interpolating the non-uniformly spaced beat locations. The recordings consist of both vocal and instrumental music recordings representative of the present day performance practice. All recordings contain percussion accompaniment, mainly the Mridangam. &lt;br /&gt;
&lt;br /&gt;
'''HJDB''' (to be confirmed)&lt;br /&gt;
The HJDB dataset contains '''236''' excerpts of Hardcore, Jungle and Drum and Bass music between 30s and 2 minutes in length. All excerpts are in 4/4 and have a constant tempo. &lt;br /&gt;
For further information see Hockman et al (2012).&lt;br /&gt;
&lt;br /&gt;
In total this makes '''1357''' excerpts (of which 259 are full length songs).&lt;br /&gt;
&lt;br /&gt;
=== Audio Formats ===&lt;br /&gt;
&lt;br /&gt;
The data are monophonic sound files&lt;br /&gt;
&lt;br /&gt;
* CD-quality (PCM, 16-bit, 44100 Hz) for all except Ballroom (originally lower quality, but resampled to 44100 Hz)&lt;br /&gt;
* single channel (mono)&lt;br /&gt;
&lt;br /&gt;
== Submission Format ==&lt;br /&gt;
Submissions to this task will have to conform to a specified format detailed below. Submissions should be packaged and contain at least two files: The algorithm itself and a README containing contact information and detailing, in full, the use of the algorithm.&lt;br /&gt;
&lt;br /&gt;
=== Input Data ===&lt;br /&gt;
Participating algorithms will have to read audio in the following format:&lt;br /&gt;
&lt;br /&gt;
* Sample rate: 44.1 KHz&lt;br /&gt;
* Sample size: 16 bit&lt;br /&gt;
* Number of channels: 1 (mono)&lt;br /&gt;
* Encoding: WAV &lt;br /&gt;
&lt;br /&gt;
=== Output Data ===&lt;br /&gt;
&lt;br /&gt;
The downbeat estimation algorithms will return downbeat times in an ASCII text file for each input .wav audio file. The specification of this output file is immediately below.&lt;br /&gt;
&lt;br /&gt;
=== Output File Format (Audio Downbeat Estimation) ===&lt;br /&gt;
&lt;br /&gt;
The downbeat output file format is an ASCII text format. Each downbeat time is specified, in seconds, on its own line. Specifically, &lt;br /&gt;
&lt;br /&gt;
 &amp;lt;downbeat time (in seconds)&amp;gt;\n&lt;br /&gt;
&lt;br /&gt;
where \n denotes the end of line. The &amp;lt; and &amp;gt; characters are not included. An example output file would look something like:&lt;br /&gt;
&lt;br /&gt;
 0.243&lt;br /&gt;
 1.486&lt;br /&gt;
 2.729&lt;br /&gt;
&lt;br /&gt;
=== Algorithm Calling Format ===&lt;br /&gt;
&lt;br /&gt;
The submitted algorithm must take as arguments a SINGLE .wav file to perform the downbeat estimation as well as the full output path and filename of the output file. The ability to specify the output path and file name is essential. Denoting the input .wav file path and name as %input and the output file path and name as %output, a program called foobar could be called from the command-line as follows:&lt;br /&gt;
&lt;br /&gt;
 foobar %input %output&lt;br /&gt;
 foobar -i %input -o %output&lt;br /&gt;
&lt;br /&gt;
Moreover, if your submission takes additional parameters, such as a detection threshold, foobar could be called like:&lt;br /&gt;
&lt;br /&gt;
 foobar .1 %input %output&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output  &lt;br /&gt;
&lt;br /&gt;
If your submission is in MATLAB, it should be submitted as a function. Once again, the function must contain String inputs for the full path and names of the input and output files. Parameters could also be specified as input arguments of the function. For example: &lt;br /&gt;
&lt;br /&gt;
 foobar('%input','%output')&lt;br /&gt;
 foobar(.1,'%input','%output')&lt;br /&gt;
&lt;br /&gt;
=== README File ===&lt;br /&gt;
&lt;br /&gt;
A README file accompanying each submission should contain explicit instructions on how to to run the program (as well as contact information, etc.). In particular, each command line to run should be specified, using %input for the input sound file and %output for the resulting text file.&lt;br /&gt;
&lt;br /&gt;
For instance, to test the program foobar with different values for parameters param1, the README file would look like:&lt;br /&gt;
&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output&lt;br /&gt;
 foobar -param1 .15 -i %input -o %output&lt;br /&gt;
 foobar -param1 .2 -i %input -o %output&lt;br /&gt;
 foobar -param1 .25 -i %input -o %output&lt;br /&gt;
 foobar -param1 .3 -i %input -o %output&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
For a submission using MATLAB, the README file could look like:&lt;br /&gt;
&lt;br /&gt;
 matlab -r &amp;quot;foobar(.1,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.15,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.2,'%input','%output');quit;&amp;quot; &lt;br /&gt;
 matlab -r &amp;quot;foobar(.25,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.3,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
The different command lines to evaluate the performance of each parameter set over the whole database will be generated automatically from each line in the README file containing both '%input' and '%output' strings.&lt;br /&gt;
&lt;br /&gt;
== Evaluation Procedures ==&lt;br /&gt;
&lt;br /&gt;
For the evalution procedure we will use&lt;br /&gt;
*'''F-measure''' - the standard calculation as used in onset and beat tracking evaluation with a +/-70ms window, see Dixon (2007).&lt;br /&gt;
&lt;br /&gt;
Given the high diversity of musical styles included in the task, results will be reported per each invidiual dataset. &lt;br /&gt;
&lt;br /&gt;
== Example Data ==&lt;br /&gt;
&lt;br /&gt;
A total of 20 beat and downbeat annotated 30s excerpts are available for participants. &lt;br /&gt;
The data is available to download here:&lt;br /&gt;
&lt;br /&gt;
https://www.music-ir.org/evaluation/MIREX/data/2014/downbeat/&lt;br /&gt;
&lt;br /&gt;
User: downbeat &lt;br /&gt;
&lt;br /&gt;
Password: d0wn63at&lt;br /&gt;
&lt;br /&gt;
Due to the availability of the Ballroom and Beatles datasets, we only include examples of the remaining styles as follows:&lt;br /&gt;
&lt;br /&gt;
'''HJDB''': 5 excerpts&lt;br /&gt;
&lt;br /&gt;
'''Cretan''': 3 excerpts&lt;br /&gt;
&lt;br /&gt;
'''Carnatic''': 4 excerpts&lt;br /&gt;
&lt;br /&gt;
'''Turkish''': 8 excerpts (2x Aksak, 2x Curcuna, 2x Düyek, and 2x Sofyan).&lt;br /&gt;
&lt;br /&gt;
Please note the audio files are in .flac format&lt;br /&gt;
&lt;br /&gt;
== Time and hardware limits ==&lt;br /&gt;
Due to the potentially high number of participants in this and other audio tasks, hard limits on the runtime of submissions will be imposed.&lt;br /&gt;
&lt;br /&gt;
A hard limit of 24 hours will be imposed on analysis times. Submissions exceeding this limit may not receive a result.&lt;br /&gt;
&lt;br /&gt;
== Potential Participants ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
Jose R. Zapata / joser.zapata (at) upb.edu.co&lt;br /&gt;
&lt;br /&gt;
== Discussion ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
= Bibliography =&lt;br /&gt;
&lt;br /&gt;
S. Dixon, F. Gouyon and G. Widmer, [http://ismir2004.ismir.net/proceedings/p093-page-509-paper165.pdf Towards Characterisation of Music via Rhythmic Patterns], In Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR 2004), pp 509-516.&lt;br /&gt;
&lt;br /&gt;
S. Dixon, [http://www.eecs.qmul.ac.uk/~simond/pub/2007/jnmr07.pdf Evaluation of audio beat tracking system BeatRoot], Journal of New Music Research, vol. 36, no. 1, pp. 39-51, 2007.&lt;br /&gt;
&lt;br /&gt;
J. A. Hockman, M. E. P. Davies, I. Fujinaga.[http://ismir2012.ismir.net/event/papers/169-ismir-2012.pdf ONE IN THE JUNGLE: Downbeat Detection in Hardcore, Jungle, and Drum and Bass], In Proceedings of 13th International Society for Music Information Retrieval Conference (ISMIR), Porto, Portugal pp. 169-174, 2012.&lt;br /&gt;
&lt;br /&gt;
F. Krebs, S. Boeck, and G. Widmer, [http://www.cp.jku.at/research/papers/Krebs_etal_ISMIR_2013.pdf Rhythmic Pattern Modeling for Beat- and Downbeat Tracking in Musical Audio], In Proceedings of 14th International Society for Music Information Retrieval Conference (ISMIR), Curitiba, Brazil, 2013.&lt;br /&gt;
&lt;br /&gt;
M. Mauch, C. Cannam, M. E. P. Davies, S. Dixon, C. Harte, S. Kolozali and D. Tidhar, [http://ismir2009.ismir.net/proceedings/LBD-18.pdf OMRAS2 Metadata Project 2009], Late-breaking session at the 10th International Conference on Music Information Retrieval, 2009.&lt;br /&gt;
&lt;br /&gt;
A. Srinivasamurthy, A. Holzapfel, and Xavier Serra, [http://www.tandfonline.com/doi/full/10.1080/09298215.2013.879902 In Search of Automatic Rhythm Analysis Methods for Turkish and Indian Art Music], Journal of New Music Research, vol. 43, no. 1, pp. 94-114, 2014.&lt;/div&gt;</summary>
		<author><name>Matthew Davies</name></author>
		
	</entry>
	<entry>
		<id>https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10293</id>
		<title>2014:Audio Downbeat Estimation</title>
		<link rel="alternate" type="text/html" href="https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10293"/>
		<updated>2014-07-22T18:23:57Z</updated>

		<summary type="html">&lt;p&gt;Matthew Davies: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Description ==&lt;br /&gt;
&lt;br /&gt;
'''This task is new for 2014!'''&lt;br /&gt;
&lt;br /&gt;
This text has been adapted from the Audio Beat Tracking Wiki page.  Please add your comments and discussion at the bottom of this page.&lt;br /&gt;
&lt;br /&gt;
The aim of the automatic downbeat estimation task is to identify the locations of downbeats in a collection of sound files. While this is similar to the Audio Beat Tracking task, here the aim is to find the first beat of each bar (measure) rather than all beat times. Algorithms are '''not''' required to estimate beat times or time-signature in addition to downbeats.&lt;br /&gt;
&lt;br /&gt;
Submitted algorithms will be evaluated in terms of their accuracy in finding downbeat locations (only) as annotated by musical experts across several diverse datasets.&lt;br /&gt;
&lt;br /&gt;
'''Update 22/07/14''' A small set of training data is now available. Please see [[#Example_Data]]&lt;br /&gt;
&lt;br /&gt;
== Data ==&lt;br /&gt;
&lt;br /&gt;
=== Collections ===&lt;br /&gt;
'''Ballroom'''&lt;br /&gt;
The ballroom dataset contains eight different dance styles (Cha Cha, Jive, Quickstep, Rumba, Samba, Tango, Viennese Waltz and Waltz). It consists of '''697''' excerpts of 30s in duration.&lt;br /&gt;
The dataset contains two different meters 3/4 and 4/4 - but all pieces have constant meter. For further information see Dixon et al (2004) and Krebs et al (2013).&lt;br /&gt;
Note, we are using the ground truth annotations from Krebs et al. (2013) available at https://github.com/CPJKU/BallroomAnnotations&lt;br /&gt;
&lt;br /&gt;
'''Isophonics (Beatles only)'''&lt;br /&gt;
The Beatles dataset from the Centre for Digital Music at Queen Mary, University of London (http://www.isophonics.net/), as also used for Audio Chord Estimation in MIREX for many years. &lt;br /&gt;
This dataset contains '''179''' complete songs (all except Revolution 9), the majority of which are in 4/4.&lt;br /&gt;
For further information see Mauch et al (2009).&lt;br /&gt;
&lt;br /&gt;
'''Turkish Data'''&lt;br /&gt;
The Turkish corpus is an extended version of the annotated data used in Srinivasamurthy et al. (2014). It includes '''85''' excerpts of one&lt;br /&gt;
minute length each, and each piece belongs to one of four&lt;br /&gt;
rhythm classes that are referred to as usul in Turkish Art&lt;br /&gt;
music. 30 pieces are in the 9/8-usul Aksak, 18 pieces&lt;br /&gt;
in the 10/8-usul Curcuna, 28 samples in the 8/8-usul&lt;br /&gt;
Düyek, and 9 samples in the 4/4 Sofyan.&lt;br /&gt;
&lt;br /&gt;
'''Cretan Data'''&lt;br /&gt;
The corpus of Cretan music consists of '''42''' full length pieces of Cretan leaping dances. While there are several dances that differ in terms of their steps, the differences in&lt;br /&gt;
the sound are most noticeable in the melodic content, and all pieces can be considered to belong to one rhythmic style. All these dances are usually notated using a 2/4 time signature,&lt;br /&gt;
and the accompanying rhythmical patterns are usually played on a Cretan lute. While a variety of rhythmic patterns exist, they do not relate to a specific dance and can be&lt;br /&gt;
assumed to occur in all of the 42 songs in this corpus.&lt;br /&gt;
&lt;br /&gt;
'''Carnatic Data'''&lt;br /&gt;
The Carnatic music dataset is a subset of the CompMusic [http://compmusic.upf.edu/carnatic-rhythm-dataset Carnatic Music Rhythm Dataset]. It includes '''118''' two minute long excerpts spanning four most commonly used tālas (the rhythmic framework of Carnatic music, consisting of time cycles) of Carnatic music. There are 30 examples in each of ādi tāla (8 beats/cycle), rūpaka tāla (3 beats/cycle) and miśra chāpu tāla (7 beats/cycle), and 28 examples in khaṇḍa chāpu tāla (5 beats/cycle). The beats of the tāla in miśra chāpu and khaṇḍa chāpu are non-uniform, but for consistency with other datasets, a uniform beat pulse was obtained by interpolating the non-uniformly spaced beat locations. The recordings consist of both vocal and instrumental music recordings representative of the present day performance practice. All recordings contain percussion accompaniment, mainly the Mridangam. &lt;br /&gt;
&lt;br /&gt;
'''HJDB''' (to be confirmed)&lt;br /&gt;
The HJDB dataset contains '''236''' excerpts of Hardcore, Jungle and Drum and Bass music between 30s and 2 minutes in length. All excerpts are in 4/4 and have a constant tempo. &lt;br /&gt;
For further information see Hockman et al (2012).&lt;br /&gt;
&lt;br /&gt;
In total this makes '''1357''' excerpts (of which 259 are full length songs).&lt;br /&gt;
&lt;br /&gt;
=== Audio Formats ===&lt;br /&gt;
&lt;br /&gt;
The data are monophonic sound files&lt;br /&gt;
&lt;br /&gt;
* CD-quality (PCM, 16-bit, 44100 Hz) for all except Ballroom (originally lower quality, but resampled to 44100 Hz)&lt;br /&gt;
* single channel (mono)&lt;br /&gt;
&lt;br /&gt;
== Submission Format ==&lt;br /&gt;
Submissions to this task will have to conform to a specified format detailed below. Submissions should be packaged and contain at least two files: The algorithm itself and a README containing contact information and detailing, in full, the use of the algorithm.&lt;br /&gt;
&lt;br /&gt;
=== Input Data ===&lt;br /&gt;
Participating algorithms will have to read audio in the following format:&lt;br /&gt;
&lt;br /&gt;
* Sample rate: 44.1 KHz&lt;br /&gt;
* Sample size: 16 bit&lt;br /&gt;
* Number of channels: 1 (mono)&lt;br /&gt;
* Encoding: WAV &lt;br /&gt;
&lt;br /&gt;
=== Output Data ===&lt;br /&gt;
&lt;br /&gt;
The downbeat estimation algorithms will return downbeat times in an ASCII text file for each input .wav audio file. The specification of this output file is immediately below.&lt;br /&gt;
&lt;br /&gt;
=== Output File Format (Audio Downbeat Estimation) ===&lt;br /&gt;
&lt;br /&gt;
The downbeat output file format is an ASCII text format. Each downbeat time is specified, in seconds, on its own line. Specifically, &lt;br /&gt;
&lt;br /&gt;
 &amp;lt;downbeat time (in seconds)&amp;gt;\n&lt;br /&gt;
&lt;br /&gt;
where \n denotes the end of line. The &amp;lt; and &amp;gt; characters are not included. An example output file would look something like:&lt;br /&gt;
&lt;br /&gt;
 0.243&lt;br /&gt;
 1.486&lt;br /&gt;
 2.729&lt;br /&gt;
&lt;br /&gt;
=== Algorithm Calling Format ===&lt;br /&gt;
&lt;br /&gt;
The submitted algorithm must take as arguments a SINGLE .wav file to perform the downbeat estimation as well as the full output path and filename of the output file. The ability to specify the output path and file name is essential. Denoting the input .wav file path and name as %input and the output file path and name as %output, a program called foobar could be called from the command-line as follows:&lt;br /&gt;
&lt;br /&gt;
 foobar %input %output&lt;br /&gt;
 foobar -i %input -o %output&lt;br /&gt;
&lt;br /&gt;
Moreover, if your submission takes additional parameters, such as a detection threshold, foobar could be called like:&lt;br /&gt;
&lt;br /&gt;
 foobar .1 %input %output&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output  &lt;br /&gt;
&lt;br /&gt;
If your submission is in MATLAB, it should be submitted as a function. Once again, the function must contain String inputs for the full path and names of the input and output files. Parameters could also be specified as input arguments of the function. For example: &lt;br /&gt;
&lt;br /&gt;
 foobar('%input','%output')&lt;br /&gt;
 foobar(.1,'%input','%output')&lt;br /&gt;
&lt;br /&gt;
=== README File ===&lt;br /&gt;
&lt;br /&gt;
A README file accompanying each submission should contain explicit instructions on how to to run the program (as well as contact information, etc.). In particular, each command line to run should be specified, using %input for the input sound file and %output for the resulting text file.&lt;br /&gt;
&lt;br /&gt;
For instance, to test the program foobar with different values for parameters param1, the README file would look like:&lt;br /&gt;
&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output&lt;br /&gt;
 foobar -param1 .15 -i %input -o %output&lt;br /&gt;
 foobar -param1 .2 -i %input -o %output&lt;br /&gt;
 foobar -param1 .25 -i %input -o %output&lt;br /&gt;
 foobar -param1 .3 -i %input -o %output&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
For a submission using MATLAB, the README file could look like:&lt;br /&gt;
&lt;br /&gt;
 matlab -r &amp;quot;foobar(.1,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.15,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.2,'%input','%output');quit;&amp;quot; &lt;br /&gt;
 matlab -r &amp;quot;foobar(.25,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.3,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
The different command lines to evaluate the performance of each parameter set over the whole database will be generated automatically from each line in the README file containing both '%input' and '%output' strings.&lt;br /&gt;
&lt;br /&gt;
== Evaluation Procedures ==&lt;br /&gt;
&lt;br /&gt;
For the evalution procedure we will use&lt;br /&gt;
*'''F-measure''' - the standard calculation as used in onset and beat tracking evaluation with a +/-70ms window, see Dixon (2007).&lt;br /&gt;
&lt;br /&gt;
Given the high diversity of musical styles included in the task, results will be reported per each invidiual dataset. &lt;br /&gt;
&lt;br /&gt;
== Example Data ==&lt;br /&gt;
&lt;br /&gt;
A total of 20 beat and downbeat annotated 30s excerpts are available for participants. &lt;br /&gt;
The data is available to download here:&lt;br /&gt;
&lt;br /&gt;
https://www.music-ir.org/evaluation/MIREX/data/2014/downbeat/&lt;br /&gt;
&lt;br /&gt;
User: downbeat Password: d0wn63at&lt;br /&gt;
&lt;br /&gt;
Due to the availability of the Ballroom and Beatles datasets, we only include examples of the remaining styles as follows:&lt;br /&gt;
&lt;br /&gt;
'''HJDB''': 5 excerpts&lt;br /&gt;
&lt;br /&gt;
'''Cretan''': 3 excerpts&lt;br /&gt;
&lt;br /&gt;
'''Carnatic''': 4 excerpts&lt;br /&gt;
&lt;br /&gt;
'''Turkish''': 8 excerpts (2x Aksak, 2x Curcuna, 2x Düyek, and 2x Sofyan).&lt;br /&gt;
&lt;br /&gt;
Please note the audio files are in .flac format&lt;br /&gt;
&lt;br /&gt;
== Time and hardware limits ==&lt;br /&gt;
Due to the potentially high number of participants in this and other audio tasks, hard limits on the runtime of submissions will be imposed.&lt;br /&gt;
&lt;br /&gt;
A hard limit of 24 hours will be imposed on analysis times. Submissions exceeding this limit may not receive a result.&lt;br /&gt;
&lt;br /&gt;
== Potential Participants ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
Jose R. Zapata / joser.zapata (at) upb.edu.co&lt;br /&gt;
&lt;br /&gt;
== Discussion ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
= Bibliography =&lt;br /&gt;
&lt;br /&gt;
S. Dixon, F. Gouyon and G. Widmer, [http://ismir2004.ismir.net/proceedings/p093-page-509-paper165.pdf Towards Characterisation of Music via Rhythmic Patterns], In Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR 2004), pp 509-516.&lt;br /&gt;
&lt;br /&gt;
S. Dixon, [http://www.eecs.qmul.ac.uk/~simond/pub/2007/jnmr07.pdf Evaluation of audio beat tracking system BeatRoot], Journal of New Music Research, vol. 36, no. 1, pp. 39-51, 2007.&lt;br /&gt;
&lt;br /&gt;
J. A. Hockman, M. E. P. Davies, I. Fujinaga.[http://ismir2012.ismir.net/event/papers/169-ismir-2012.pdf ONE IN THE JUNGLE: Downbeat Detection in Hardcore, Jungle, and Drum and Bass], In Proceedings of 13th International Society for Music Information Retrieval Conference (ISMIR), Porto, Portugal pp. 169-174, 2012.&lt;br /&gt;
&lt;br /&gt;
F. Krebs, S. Boeck, and G. Widmer, [http://www.cp.jku.at/research/papers/Krebs_etal_ISMIR_2013.pdf Rhythmic Pattern Modeling for Beat- and Downbeat Tracking in Musical Audio], In Proceedings of 14th International Society for Music Information Retrieval Conference (ISMIR), Curitiba, Brazil, 2013.&lt;br /&gt;
&lt;br /&gt;
M. Mauch, C. Cannam, M. E. P. Davies, S. Dixon, C. Harte, S. Kolozali and D. Tidhar, [http://ismir2009.ismir.net/proceedings/LBD-18.pdf OMRAS2 Metadata Project 2009], Late-breaking session at the 10th International Conference on Music Information Retrieval, 2009.&lt;br /&gt;
&lt;br /&gt;
A. Srinivasamurthy, A. Holzapfel, and Xavier Serra, [http://www.tandfonline.com/doi/full/10.1080/09298215.2013.879902 In Search of Automatic Rhythm Analysis Methods for Turkish and Indian Art Music], Journal of New Music Research, vol. 43, no. 1, pp. 94-114, 2014.&lt;/div&gt;</summary>
		<author><name>Matthew Davies</name></author>
		
	</entry>
	<entry>
		<id>https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10292</id>
		<title>2014:Audio Downbeat Estimation</title>
		<link rel="alternate" type="text/html" href="https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10292"/>
		<updated>2014-07-22T18:20:59Z</updated>

		<summary type="html">&lt;p&gt;Matthew Davies: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Description ==&lt;br /&gt;
&lt;br /&gt;
'''This task is new for 2014!'''&lt;br /&gt;
&lt;br /&gt;
This text has been adapted from the Audio Beat Tracking Wiki page.  Please add your comments and discussion at the bottom of this page.&lt;br /&gt;
&lt;br /&gt;
The aim of the automatic downbeat estimation task is to identify the locations of downbeats in a collection of sound files. While this is similar to the Audio Beat Tracking task, here the aim is to find the first beat of each bar (measure) rather than all beat times. Algorithms are '''not''' required to estimate beat times or time-signature in addition to downbeats.&lt;br /&gt;
&lt;br /&gt;
Submitted algorithms will be evaluated in terms of their accuracy in finding downbeat locations (only) as annotated by musical experts across several diverse datasets.&lt;br /&gt;
&lt;br /&gt;
'''Update 22/07/14''' A small set of training data is now available. Please see &lt;br /&gt;
&lt;br /&gt;
== Data ==&lt;br /&gt;
&lt;br /&gt;
=== Collections ===&lt;br /&gt;
'''Ballroom'''&lt;br /&gt;
The ballroom dataset contains eight different dance styles (Cha Cha, Jive, Quickstep, Rumba, Samba, Tango, Viennese Waltz and Waltz). It consists of '''697''' excerpts of 30s in duration.&lt;br /&gt;
The dataset contains two different meters 3/4 and 4/4 - but all pieces have constant meter. For further information see Dixon et al (2004) and Krebs et al (2013).&lt;br /&gt;
Note, we are using the ground truth annotations from Krebs et al. (2013) available at https://github.com/CPJKU/BallroomAnnotations&lt;br /&gt;
&lt;br /&gt;
'''Isophonics (Beatles only)'''&lt;br /&gt;
The Beatles dataset from the Centre for Digital Music at Queen Mary, University of London (http://www.isophonics.net/), as also used for Audio Chord Estimation in MIREX for many years. &lt;br /&gt;
This dataset contains '''179''' complete songs (all except Revolution 9), the majority of which are in 4/4.&lt;br /&gt;
For further information see Mauch et al (2009).&lt;br /&gt;
&lt;br /&gt;
'''Turkish Data'''&lt;br /&gt;
The Turkish corpus is an extended version of the annotated data used in Srinivasamurthy et al. (2014). It includes '''85''' excerpts of one&lt;br /&gt;
minute length each, and each piece belongs to one of four&lt;br /&gt;
rhythm classes that are referred to as usul in Turkish Art&lt;br /&gt;
music. 30 pieces are in the 9/8-usul Aksak, 18 pieces&lt;br /&gt;
in the 10/8-usul Curcuna, 28 samples in the 8/8-usul&lt;br /&gt;
Düyek, and 9 samples in the 4/4 Sofyan.&lt;br /&gt;
&lt;br /&gt;
'''Cretan Data'''&lt;br /&gt;
The corpus of Cretan music consists of '''42''' full length pieces of Cretan leaping dances. While there are several dances that differ in terms of their steps, the differences in&lt;br /&gt;
the sound are most noticeable in the melodic content, and all pieces can be considered to belong to one rhythmic style. All these dances are usually notated using a 2/4 time signature,&lt;br /&gt;
and the accompanying rhythmical patterns are usually played on a Cretan lute. While a variety of rhythmic patterns exist, they do not relate to a specific dance and can be&lt;br /&gt;
assumed to occur in all of the 42 songs in this corpus.&lt;br /&gt;
&lt;br /&gt;
'''Carnatic Data'''&lt;br /&gt;
The Carnatic music dataset is a subset of the CompMusic [http://compmusic.upf.edu/carnatic-rhythm-dataset Carnatic Music Rhythm Dataset]. It includes '''118''' two minute long excerpts spanning four most commonly used tālas (the rhythmic framework of Carnatic music, consisting of time cycles) of Carnatic music. There are 30 examples in each of ādi tāla (8 beats/cycle), rūpaka tāla (3 beats/cycle) and miśra chāpu tāla (7 beats/cycle), and 28 examples in khaṇḍa chāpu tāla (5 beats/cycle). The beats of the tāla in miśra chāpu and khaṇḍa chāpu are non-uniform, but for consistency with other datasets, a uniform beat pulse was obtained by interpolating the non-uniformly spaced beat locations. The recordings consist of both vocal and instrumental music recordings representative of the present day performance practice. All recordings contain percussion accompaniment, mainly the Mridangam. &lt;br /&gt;
&lt;br /&gt;
'''HJDB''' (to be confirmed)&lt;br /&gt;
The HJDB dataset contains '''236''' excerpts of Hardcore, Jungle and Drum and Bass music between 30s and 2 minutes in length. All excerpts are in 4/4 and have a constant tempo. &lt;br /&gt;
For further information see Hockman et al (2012).&lt;br /&gt;
&lt;br /&gt;
In total this makes '''1357''' excerpts (of which 259 are full length songs).&lt;br /&gt;
&lt;br /&gt;
=== Audio Formats ===&lt;br /&gt;
&lt;br /&gt;
The data are monophonic sound files&lt;br /&gt;
&lt;br /&gt;
* CD-quality (PCM, 16-bit, 44100 Hz) for all except Ballroom (originally lower quality, but resampled to 44100 Hz)&lt;br /&gt;
* single channel (mono)&lt;br /&gt;
&lt;br /&gt;
== Submission Format ==&lt;br /&gt;
Submissions to this task will have to conform to a specified format detailed below. Submissions should be packaged and contain at least two files: The algorithm itself and a README containing contact information and detailing, in full, the use of the algorithm.&lt;br /&gt;
&lt;br /&gt;
=== Input Data ===&lt;br /&gt;
Participating algorithms will have to read audio in the following format:&lt;br /&gt;
&lt;br /&gt;
* Sample rate: 44.1 KHz&lt;br /&gt;
* Sample size: 16 bit&lt;br /&gt;
* Number of channels: 1 (mono)&lt;br /&gt;
* Encoding: WAV &lt;br /&gt;
&lt;br /&gt;
=== Output Data ===&lt;br /&gt;
&lt;br /&gt;
The downbeat estimation algorithms will return downbeat times in an ASCII text file for each input .wav audio file. The specification of this output file is immediately below.&lt;br /&gt;
&lt;br /&gt;
=== Output File Format (Audio Downbeat Estimation) ===&lt;br /&gt;
&lt;br /&gt;
The downbeat output file format is an ASCII text format. Each downbeat time is specified, in seconds, on its own line. Specifically, &lt;br /&gt;
&lt;br /&gt;
 &amp;lt;downbeat time (in seconds)&amp;gt;\n&lt;br /&gt;
&lt;br /&gt;
where \n denotes the end of line. The &amp;lt; and &amp;gt; characters are not included. An example output file would look something like:&lt;br /&gt;
&lt;br /&gt;
 0.243&lt;br /&gt;
 1.486&lt;br /&gt;
 2.729&lt;br /&gt;
&lt;br /&gt;
=== Algorithm Calling Format ===&lt;br /&gt;
&lt;br /&gt;
The submitted algorithm must take as arguments a SINGLE .wav file to perform the downbeat estimation as well as the full output path and filename of the output file. The ability to specify the output path and file name is essential. Denoting the input .wav file path and name as %input and the output file path and name as %output, a program called foobar could be called from the command-line as follows:&lt;br /&gt;
&lt;br /&gt;
 foobar %input %output&lt;br /&gt;
 foobar -i %input -o %output&lt;br /&gt;
&lt;br /&gt;
Moreover, if your submission takes additional parameters, such as a detection threshold, foobar could be called like:&lt;br /&gt;
&lt;br /&gt;
 foobar .1 %input %output&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output  &lt;br /&gt;
&lt;br /&gt;
If your submission is in MATLAB, it should be submitted as a function. Once again, the function must contain String inputs for the full path and names of the input and output files. Parameters could also be specified as input arguments of the function. For example: &lt;br /&gt;
&lt;br /&gt;
 foobar('%input','%output')&lt;br /&gt;
 foobar(.1,'%input','%output')&lt;br /&gt;
&lt;br /&gt;
=== README File ===&lt;br /&gt;
&lt;br /&gt;
A README file accompanying each submission should contain explicit instructions on how to to run the program (as well as contact information, etc.). In particular, each command line to run should be specified, using %input for the input sound file and %output for the resulting text file.&lt;br /&gt;
&lt;br /&gt;
For instance, to test the program foobar with different values for parameters param1, the README file would look like:&lt;br /&gt;
&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output&lt;br /&gt;
 foobar -param1 .15 -i %input -o %output&lt;br /&gt;
 foobar -param1 .2 -i %input -o %output&lt;br /&gt;
 foobar -param1 .25 -i %input -o %output&lt;br /&gt;
 foobar -param1 .3 -i %input -o %output&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
For a submission using MATLAB, the README file could look like:&lt;br /&gt;
&lt;br /&gt;
 matlab -r &amp;quot;foobar(.1,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.15,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.2,'%input','%output');quit;&amp;quot; &lt;br /&gt;
 matlab -r &amp;quot;foobar(.25,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.3,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
The different command lines to evaluate the performance of each parameter set over the whole database will be generated automatically from each line in the README file containing both '%input' and '%output' strings.&lt;br /&gt;
&lt;br /&gt;
== Evaluation Procedures ==&lt;br /&gt;
&lt;br /&gt;
For the evalution procedure we will use&lt;br /&gt;
*'''F-measure''' - the standard calculation as used in onset and beat tracking evaluation with a +/-70ms window, see Dixon (2007).&lt;br /&gt;
&lt;br /&gt;
Given the high diversity of musical styles included in the task, results will be reported per each invidiual dataset. &lt;br /&gt;
&lt;br /&gt;
== Example Data ==&lt;br /&gt;
&lt;br /&gt;
A total of 20 beat and downbeat annotated 30s excerpts are available for participants. &lt;br /&gt;
The data is available to download here:&lt;br /&gt;
&lt;br /&gt;
https://www.music-ir.org/evaluation/MIREX/data/2014/downbeat/&lt;br /&gt;
&lt;br /&gt;
User: downbeat Password: d0wn63at&lt;br /&gt;
&lt;br /&gt;
Due to the availability of the Ballroom and Beatles datasets, we only include examples of the remaining styles as follows:&lt;br /&gt;
&lt;br /&gt;
'''HJDB''': 5 excerpts&lt;br /&gt;
&lt;br /&gt;
'''Cretan''': 3 excerpts&lt;br /&gt;
&lt;br /&gt;
'''Carnatic''': 4 excerpts&lt;br /&gt;
&lt;br /&gt;
'''Turkish''': 8 excerpts (2x Aksak, 2x Curcuna, 2x Düyek, and 2x Sofyan).&lt;br /&gt;
&lt;br /&gt;
Please note the audio files are in .flac format&lt;br /&gt;
&lt;br /&gt;
== Time and hardware limits ==&lt;br /&gt;
Due to the potentially high number of participants in this and other audio tasks, hard limits on the runtime of submissions will be imposed.&lt;br /&gt;
&lt;br /&gt;
A hard limit of 24 hours will be imposed on analysis times. Submissions exceeding this limit may not receive a result.&lt;br /&gt;
&lt;br /&gt;
== Potential Participants ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
Jose R. Zapata / joser.zapata (at) upb.edu.co&lt;br /&gt;
&lt;br /&gt;
== Discussion ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
= Bibliography =&lt;br /&gt;
&lt;br /&gt;
S. Dixon, F. Gouyon and G. Widmer, [http://ismir2004.ismir.net/proceedings/p093-page-509-paper165.pdf Towards Characterisation of Music via Rhythmic Patterns], In Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR 2004), pp 509-516.&lt;br /&gt;
&lt;br /&gt;
S. Dixon, [http://www.eecs.qmul.ac.uk/~simond/pub/2007/jnmr07.pdf Evaluation of audio beat tracking system BeatRoot], Journal of New Music Research, vol. 36, no. 1, pp. 39-51, 2007.&lt;br /&gt;
&lt;br /&gt;
J. A. Hockman, M. E. P. Davies, I. Fujinaga.[http://ismir2012.ismir.net/event/papers/169-ismir-2012.pdf ONE IN THE JUNGLE: Downbeat Detection in Hardcore, Jungle, and Drum and Bass], In Proceedings of 13th International Society for Music Information Retrieval Conference (ISMIR), Porto, Portugal pp. 169-174, 2012.&lt;br /&gt;
&lt;br /&gt;
F. Krebs, S. Boeck, and G. Widmer, [http://www.cp.jku.at/research/papers/Krebs_etal_ISMIR_2013.pdf Rhythmic Pattern Modeling for Beat- and Downbeat Tracking in Musical Audio], In Proceedings of 14th International Society for Music Information Retrieval Conference (ISMIR), Curitiba, Brazil, 2013.&lt;br /&gt;
&lt;br /&gt;
M. Mauch, C. Cannam, M. E. P. Davies, S. Dixon, C. Harte, S. Kolozali and D. Tidhar, [http://ismir2009.ismir.net/proceedings/LBD-18.pdf OMRAS2 Metadata Project 2009], Late-breaking session at the 10th International Conference on Music Information Retrieval, 2009.&lt;br /&gt;
&lt;br /&gt;
A. Srinivasamurthy, A. Holzapfel, and Xavier Serra, [http://www.tandfonline.com/doi/full/10.1080/09298215.2013.879902 In Search of Automatic Rhythm Analysis Methods for Turkish and Indian Art Music], Journal of New Music Research, vol. 43, no. 1, pp. 94-114, 2014.&lt;/div&gt;</summary>
		<author><name>Matthew Davies</name></author>
		
	</entry>
	<entry>
		<id>https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10291</id>
		<title>2014:Audio Downbeat Estimation</title>
		<link rel="alternate" type="text/html" href="https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10291"/>
		<updated>2014-07-22T13:38:21Z</updated>

		<summary type="html">&lt;p&gt;Matthew Davies: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Description ==&lt;br /&gt;
&lt;br /&gt;
'''This task is new for 2014!'''&lt;br /&gt;
&lt;br /&gt;
This text has been adapted from the Audio Beat Tracking Wiki page.  Please add your comments and discussion at the bottom of this page.&lt;br /&gt;
&lt;br /&gt;
The aim of the automatic downbeat estimation task is to identify the locations of downbeats in a collection of sound files. While this is similar to the Audio Beat Tracking task, here the aim is to find the first beat of each bar (measure) rather than all beat times. Algorithms are '''not''' required to estimate beat times or time-signature in addition to downbeats.&lt;br /&gt;
&lt;br /&gt;
Submitted algorithms will be evaluated in terms of their accuracy in finding downbeat locations (only) as annotated by musical experts across several diverse datasets.&lt;br /&gt;
&lt;br /&gt;
'''Update 27/06/14''' A small set of training data is being prepared and will be available soon. &lt;br /&gt;
&lt;br /&gt;
== Data ==&lt;br /&gt;
&lt;br /&gt;
=== Collections ===&lt;br /&gt;
'''Ballroom'''&lt;br /&gt;
The ballroom dataset contains eight different dance styles (Cha Cha, Jive, Quickstep, Rumba, Samba, Tango, Viennese Waltz and Waltz). It consists of '''697''' excerpts of 30s in duration.&lt;br /&gt;
The dataset contains two different meters 3/4 and 4/4 - but all pieces have constant meter. For further information see Dixon et al (2004) and Krebs et al (2013).&lt;br /&gt;
Note, we are using the ground truth annotations from Krebs et al. (2013) available at https://github.com/CPJKU/BallroomAnnotations&lt;br /&gt;
&lt;br /&gt;
'''Isophonics (Beatles only)'''&lt;br /&gt;
The Beatles dataset from the Centre for Digital Music at Queen Mary, University of London (http://www.isophonics.net/), as also used for Audio Chord Estimation in MIREX for many years. &lt;br /&gt;
This dataset contains '''179''' complete songs (all except Revolution 9), the majority of which are in 4/4.&lt;br /&gt;
For further information see Mauch et al (2009).&lt;br /&gt;
&lt;br /&gt;
'''Turkish Data'''&lt;br /&gt;
The Turkish corpus is an extended version of the annotated data used in Srinivasamurthy et al. (2014). It includes '''85''' excerpts of one&lt;br /&gt;
minute length each, and each piece belongs to one of four&lt;br /&gt;
rhythm classes that are referred to as usul in Turkish Art&lt;br /&gt;
music. 30 pieces are in the 9/8-usul Aksak, 18 pieces&lt;br /&gt;
in the 10/8-usul Curcuna, 28 samples in the 8/8-usul&lt;br /&gt;
Düyek, and 9 samples in the 4/4 Sofyan.&lt;br /&gt;
&lt;br /&gt;
'''Cretan Data'''&lt;br /&gt;
The corpus of Cretan music consists of '''42''' full length pieces of Cretan leaping dances. While there are several dances that differ in terms of their steps, the differences in&lt;br /&gt;
the sound are most noticeable in the melodic content, and all pieces can be considered to belong to one rhythmic style. All these dances are usually notated using a 2/4 time signature,&lt;br /&gt;
and the accompanying rhythmical patterns are usually played on a Cretan lute. While a variety of rhythmic patterns exist, they do not relate to a specific dance and can be&lt;br /&gt;
assumed to occur in all of the 42 songs in this corpus.&lt;br /&gt;
&lt;br /&gt;
'''Carnatic Data'''&lt;br /&gt;
The Carnatic music dataset is a subset of the CompMusic [http://compmusic.upf.edu/carnatic-rhythm-dataset Carnatic Music Rhythm Dataset]. It includes '''118''' two minute long excerpts spanning four most commonly used tālas (the rhythmic framework of Carnatic music, consisting of time cycles) of Carnatic music. There are 30 examples in each of ādi tāla (8 beats/cycle), rūpaka tāla (3 beats/cycle) and miśra chāpu tāla (7 beats/cycle), and 28 examples in khaṇḍa chāpu tāla (5 beats/cycle). The beats of the tāla in miśra chāpu and khaṇḍa chāpu are non-uniform, but for consistency with other datasets, a uniform beat pulse was obtained by interpolating the non-uniformly spaced beat locations. The recordings consist of both vocal and instrumental music recordings representative of the present day performance practice. All recordings contain percussion accompaniment, mainly the Mridangam. &lt;br /&gt;
&lt;br /&gt;
'''HJDB''' (to be confirmed)&lt;br /&gt;
The HJDB dataset contains '''236''' excerpts of Hardcore, Jungle and Drum and Bass music between 30s and 2 minutes in length. All excerpts are in 4/4 and have a constant tempo. &lt;br /&gt;
For further information see Hockman et al (2012).&lt;br /&gt;
&lt;br /&gt;
In total this makes '''1357''' excerpts (of which 259 are full length songs).&lt;br /&gt;
&lt;br /&gt;
=== Audio Formats ===&lt;br /&gt;
&lt;br /&gt;
The data are monophonic sound files&lt;br /&gt;
&lt;br /&gt;
* CD-quality (PCM, 16-bit, 44100 Hz) for all except Ballroom (originally lower quality, but resampled to 44100 Hz)&lt;br /&gt;
* single channel (mono)&lt;br /&gt;
&lt;br /&gt;
== Submission Format ==&lt;br /&gt;
Submissions to this task will have to conform to a specified format detailed below. Submissions should be packaged and contain at least two files: The algorithm itself and a README containing contact information and detailing, in full, the use of the algorithm.&lt;br /&gt;
&lt;br /&gt;
=== Input Data ===&lt;br /&gt;
Participating algorithms will have to read audio in the following format:&lt;br /&gt;
&lt;br /&gt;
* Sample rate: 44.1 KHz&lt;br /&gt;
* Sample size: 16 bit&lt;br /&gt;
* Number of channels: 1 (mono)&lt;br /&gt;
* Encoding: WAV &lt;br /&gt;
&lt;br /&gt;
=== Output Data ===&lt;br /&gt;
&lt;br /&gt;
The downbeat estimation algorithms will return downbeat times in an ASCII text file for each input .wav audio file. The specification of this output file is immediately below.&lt;br /&gt;
&lt;br /&gt;
=== Output File Format (Audio Downbeat Estimation) ===&lt;br /&gt;
&lt;br /&gt;
The downbeat output file format is an ASCII text format. Each downbeat time is specified, in seconds, on its own line. Specifically, &lt;br /&gt;
&lt;br /&gt;
 &amp;lt;downbeat time (in seconds)&amp;gt;\n&lt;br /&gt;
&lt;br /&gt;
where \n denotes the end of line. The &amp;lt; and &amp;gt; characters are not included. An example output file would look something like:&lt;br /&gt;
&lt;br /&gt;
 0.243&lt;br /&gt;
 1.486&lt;br /&gt;
 2.729&lt;br /&gt;
&lt;br /&gt;
=== Algorithm Calling Format ===&lt;br /&gt;
&lt;br /&gt;
The submitted algorithm must take as arguments a SINGLE .wav file to perform the downbeat estimation as well as the full output path and filename of the output file. The ability to specify the output path and file name is essential. Denoting the input .wav file path and name as %input and the output file path and name as %output, a program called foobar could be called from the command-line as follows:&lt;br /&gt;
&lt;br /&gt;
 foobar %input %output&lt;br /&gt;
 foobar -i %input -o %output&lt;br /&gt;
&lt;br /&gt;
Moreover, if your submission takes additional parameters, such as a detection threshold, foobar could be called like:&lt;br /&gt;
&lt;br /&gt;
 foobar .1 %input %output&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output  &lt;br /&gt;
&lt;br /&gt;
If your submission is in MATLAB, it should be submitted as a function. Once again, the function must contain String inputs for the full path and names of the input and output files. Parameters could also be specified as input arguments of the function. For example: &lt;br /&gt;
&lt;br /&gt;
 foobar('%input','%output')&lt;br /&gt;
 foobar(.1,'%input','%output')&lt;br /&gt;
&lt;br /&gt;
=== README File ===&lt;br /&gt;
&lt;br /&gt;
A README file accompanying each submission should contain explicit instructions on how to to run the program (as well as contact information, etc.). In particular, each command line to run should be specified, using %input for the input sound file and %output for the resulting text file.&lt;br /&gt;
&lt;br /&gt;
For instance, to test the program foobar with different values for parameters param1, the README file would look like:&lt;br /&gt;
&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output&lt;br /&gt;
 foobar -param1 .15 -i %input -o %output&lt;br /&gt;
 foobar -param1 .2 -i %input -o %output&lt;br /&gt;
 foobar -param1 .25 -i %input -o %output&lt;br /&gt;
 foobar -param1 .3 -i %input -o %output&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
For a submission using MATLAB, the README file could look like:&lt;br /&gt;
&lt;br /&gt;
 matlab -r &amp;quot;foobar(.1,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.15,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.2,'%input','%output');quit;&amp;quot; &lt;br /&gt;
 matlab -r &amp;quot;foobar(.25,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.3,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
The different command lines to evaluate the performance of each parameter set over the whole database will be generated automatically from each line in the README file containing both '%input' and '%output' strings.&lt;br /&gt;
&lt;br /&gt;
== Evaluation Procedures ==&lt;br /&gt;
&lt;br /&gt;
For the evalution procedure we will use&lt;br /&gt;
*'''F-measure''' - the standard calculation as used in onset and beat tracking evaluation with a +/-70ms window, see Dixon (2007).&lt;br /&gt;
&lt;br /&gt;
Given the high diversity of musical styles included in the task, results will be reported per each invidiual dataset. &lt;br /&gt;
&lt;br /&gt;
== Time and hardware limits ==&lt;br /&gt;
Due to the potentially high number of participants in this and other audio tasks, hard limits on the runtime of submissions will be imposed.&lt;br /&gt;
&lt;br /&gt;
A hard limit of 24 hours will be imposed on analysis times. Submissions exceeding this limit may not receive a result.&lt;br /&gt;
&lt;br /&gt;
== Potential Participants ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
Jose R. Zapata / joser.zapata (at) upb.edu.co&lt;br /&gt;
&lt;br /&gt;
== Discussion ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
= Bibliography =&lt;br /&gt;
&lt;br /&gt;
S. Dixon, F. Gouyon and G. Widmer, [http://ismir2004.ismir.net/proceedings/p093-page-509-paper165.pdf Towards Characterisation of Music via Rhythmic Patterns], In Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR 2004), pp 509-516.&lt;br /&gt;
&lt;br /&gt;
S. Dixon, [http://www.eecs.qmul.ac.uk/~simond/pub/2007/jnmr07.pdf Evaluation of audio beat tracking system BeatRoot], Journal of New Music Research, vol. 36, no. 1, pp. 39-51, 2007.&lt;br /&gt;
&lt;br /&gt;
J. A. Hockman, M. E. P. Davies, I. Fujinaga.[http://ismir2012.ismir.net/event/papers/169-ismir-2012.pdf ONE IN THE JUNGLE: Downbeat Detection in Hardcore, Jungle, and Drum and Bass], In Proceedings of 13th International Society for Music Information Retrieval Conference (ISMIR), Porto, Portugal pp. 169-174, 2012.&lt;br /&gt;
&lt;br /&gt;
F. Krebs, S. Boeck, and G. Widmer, [http://www.cp.jku.at/research/papers/Krebs_etal_ISMIR_2013.pdf Rhythmic Pattern Modeling for Beat- and Downbeat Tracking in Musical Audio], In Proceedings of 14th International Society for Music Information Retrieval Conference (ISMIR), Curitiba, Brazil, 2013.&lt;br /&gt;
&lt;br /&gt;
M. Mauch, C. Cannam, M. E. P. Davies, S. Dixon, C. Harte, S. Kolozali and D. Tidhar, [http://ismir2009.ismir.net/proceedings/LBD-18.pdf OMRAS2 Metadata Project 2009], Late-breaking session at the 10th International Conference on Music Information Retrieval, 2009.&lt;br /&gt;
&lt;br /&gt;
A. Srinivasamurthy, A. Holzapfel, and Xavier Serra, [http://www.tandfonline.com/doi/full/10.1080/09298215.2013.879902 In Search of Automatic Rhythm Analysis Methods for Turkish and Indian Art Music], Journal of New Music Research, vol. 43, no. 1, pp. 94-114, 2014.&lt;/div&gt;</summary>
		<author><name>Matthew Davies</name></author>
		
	</entry>
	<entry>
		<id>https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10137</id>
		<title>2014:Audio Downbeat Estimation</title>
		<link rel="alternate" type="text/html" href="https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10137"/>
		<updated>2014-06-27T09:07:35Z</updated>

		<summary type="html">&lt;p&gt;Matthew Davies: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Description ==&lt;br /&gt;
&lt;br /&gt;
'''This task is new for 2014!'''&lt;br /&gt;
&lt;br /&gt;
This text has been adapted from the Audio Beat Tracking Wiki page.  Please add your comments and discussion at the bottom of this page.&lt;br /&gt;
&lt;br /&gt;
The aim of the automatic downbeat estimation task is to identify the locations of downbeats in a collection of sound files. While this is similar to the Audio Beat Tracking task, here the aim is to find the first beat of each bar (measure) rather than all beat times. Algorithms are '''not''' required to estimate beat times or time-signature in addition to downbeats.&lt;br /&gt;
&lt;br /&gt;
Submitted algorithms will be evaluated in terms of their accuracy in finding downbeat locations (only) as annotated by musical experts across several diverse datasets.&lt;br /&gt;
&lt;br /&gt;
'''Update 27/06/14''' A small set of training data is being prepared and will be available soon. &lt;br /&gt;
&lt;br /&gt;
== Data ==&lt;br /&gt;
&lt;br /&gt;
=== Collections ===&lt;br /&gt;
'''Ballroom'''&lt;br /&gt;
The ballroom dataset contains eight different dance styles (Cha Cha, Jive, Quickstep, Rumba, Samba, Tango, Viennese Waltz and Waltz). It consists of '''697''' excerpts of 30s in duration.&lt;br /&gt;
The dataset contains two different meters 3/4 and 4/4 - but all pieces have constant meter. For further information see Dixon et al (2004) and Krebs et al (2013).&lt;br /&gt;
Note, we are using the ground truth annotations from Krebs et al. (2013) available at https://github.com/CPJKU/BallroomAnnotations&lt;br /&gt;
&lt;br /&gt;
'''Isophonics (Beatles only)'''&lt;br /&gt;
The Beatles dataset from the Centre for Digital Music at Queen Mary, University of London (http://www.isophonics.net/), as also used for Audio Chord Estimation in MIREX for many years. &lt;br /&gt;
This dataset contains '''179''' complete songs (all except Revolution 9), the majority of which are in 4/4.&lt;br /&gt;
For further information see Mauch et al (2009).&lt;br /&gt;
&lt;br /&gt;
'''Turkish Data'''&lt;br /&gt;
The Turkish corpus is an extended version of the annotated data used in Srinivasamurthy et al. (2014). It includes '''82''' excerpts of one&lt;br /&gt;
minute length each, and each piece belongs to one of three&lt;br /&gt;
rhythm classes that are referred to as usul in Turkish Art&lt;br /&gt;
music. 32 pieces are in the 9/8-usul Aksak, 20 pieces&lt;br /&gt;
in the 10/8-usul Curcuna, and 30 samples in the 8/8-usul&lt;br /&gt;
Düyek.&lt;br /&gt;
&lt;br /&gt;
'''Cretan Data'''&lt;br /&gt;
The corpus of Cretan music consists of '''42''' full length pieces of Cretan leaping dances. While there are several dances that differ in terms of their steps, the differences in&lt;br /&gt;
the sound are most noticeable in the melodic content, and all pieces can be considered to belong to one rhythmic style. All these dances are usually notated using a 2/4 time signature,&lt;br /&gt;
and the accompanying rhythmical patterns are usually played on a Cretan lute. While a variety of rhythmic patterns exist, they do not relate to a specific dance and can be&lt;br /&gt;
assumed to occur in all of the 42 songs in this corpus.&lt;br /&gt;
&lt;br /&gt;
'''Carnatic Data'''&lt;br /&gt;
The Carnatic music dataset is a subset of the CompMusic [http://compmusic.upf.edu/carnatic-rhythm-dataset Carnatic Music Rhythm Dataset]. It includes '''118''' two minute long excerpts spanning four most commonly used tālas (the rhythmic framework of Carnatic music, consisting of time cycles) of Carnatic music. There are 30 examples in each of ādi tāla (8 beats/cycle), rūpaka tāla (3 beats/cycle) and miśra chāpu tāla (7 beats/cycle), and 28 examples in khaṇḍa chāpu tāla (5 beats/cycle). The beats of the tāla in miśra chāpu and khaṇḍa chāpu are non-uniform, but for consistency with other datasets, a uniform beat pulse was obtained by interpolating the non-uniformly spaced beat locations. The recordings consist of both vocal and instrumental music recordings representative of the present day performance practice. All recordings contain percussion accompaniment, mainly the Mridangam. &lt;br /&gt;
&lt;br /&gt;
'''HJDB''' (to be confirmed)&lt;br /&gt;
The HJDB dataset contains '''236''' excerpts of Hardcore, Jungle and Drum and Bass music between 30s and 2 minutes in length. All excerpts are in 4/4 and have a constant tempo. &lt;br /&gt;
For further information see Hockman et al (2012).&lt;br /&gt;
&lt;br /&gt;
In total this makes '''1354''' excerpts (of which 259 are full length songs).&lt;br /&gt;
&lt;br /&gt;
=== Audio Formats ===&lt;br /&gt;
&lt;br /&gt;
The data are monophonic sound files&lt;br /&gt;
&lt;br /&gt;
* CD-quality (PCM, 16-bit, 44100 Hz) for all except Ballroom (originally lower quality, but resampled to 44100 Hz)&lt;br /&gt;
* single channel (mono)&lt;br /&gt;
&lt;br /&gt;
== Submission Format ==&lt;br /&gt;
Submissions to this task will have to conform to a specified format detailed below. Submissions should be packaged and contain at least two files: The algorithm itself and a README containing contact information and detailing, in full, the use of the algorithm.&lt;br /&gt;
&lt;br /&gt;
=== Input Data ===&lt;br /&gt;
Participating algorithms will have to read audio in the following format:&lt;br /&gt;
&lt;br /&gt;
* Sample rate: 44.1 KHz&lt;br /&gt;
* Sample size: 16 bit&lt;br /&gt;
* Number of channels: 1 (mono)&lt;br /&gt;
* Encoding: WAV &lt;br /&gt;
&lt;br /&gt;
=== Output Data ===&lt;br /&gt;
&lt;br /&gt;
The downbeat estimation algorithms will return downbeat times in an ASCII text file for each input .wav audio file. The specification of this output file is immediately below.&lt;br /&gt;
&lt;br /&gt;
=== Output File Format (Audio Downbeat Estimation) ===&lt;br /&gt;
&lt;br /&gt;
The downbeat output file format is an ASCII text format. Each downbeat time is specified, in seconds, on its own line. Specifically, &lt;br /&gt;
&lt;br /&gt;
 &amp;lt;downbeat time (in seconds)&amp;gt;\n&lt;br /&gt;
&lt;br /&gt;
where \n denotes the end of line. The &amp;lt; and &amp;gt; characters are not included. An example output file would look something like:&lt;br /&gt;
&lt;br /&gt;
 0.243&lt;br /&gt;
 1.486&lt;br /&gt;
 2.729&lt;br /&gt;
&lt;br /&gt;
=== Algorithm Calling Format ===&lt;br /&gt;
&lt;br /&gt;
The submitted algorithm must take as arguments a SINGLE .wav file to perform the downbeat estimation as well as the full output path and filename of the output file. The ability to specify the output path and file name is essential. Denoting the input .wav file path and name as %input and the output file path and name as %output, a program called foobar could be called from the command-line as follows:&lt;br /&gt;
&lt;br /&gt;
 foobar %input %output&lt;br /&gt;
 foobar -i %input -o %output&lt;br /&gt;
&lt;br /&gt;
Moreover, if your submission takes additional parameters, such as a detection threshold, foobar could be called like:&lt;br /&gt;
&lt;br /&gt;
 foobar .1 %input %output&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output  &lt;br /&gt;
&lt;br /&gt;
If your submission is in MATLAB, it should be submitted as a function. Once again, the function must contain String inputs for the full path and names of the input and output files. Parameters could also be specified as input arguments of the function. For example: &lt;br /&gt;
&lt;br /&gt;
 foobar('%input','%output')&lt;br /&gt;
 foobar(.1,'%input','%output')&lt;br /&gt;
&lt;br /&gt;
=== README File ===&lt;br /&gt;
&lt;br /&gt;
A README file accompanying each submission should contain explicit instructions on how to to run the program (as well as contact information, etc.). In particular, each command line to run should be specified, using %input for the input sound file and %output for the resulting text file.&lt;br /&gt;
&lt;br /&gt;
For instance, to test the program foobar with different values for parameters param1, the README file would look like:&lt;br /&gt;
&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output&lt;br /&gt;
 foobar -param1 .15 -i %input -o %output&lt;br /&gt;
 foobar -param1 .2 -i %input -o %output&lt;br /&gt;
 foobar -param1 .25 -i %input -o %output&lt;br /&gt;
 foobar -param1 .3 -i %input -o %output&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
For a submission using MATLAB, the README file could look like:&lt;br /&gt;
&lt;br /&gt;
 matlab -r &amp;quot;foobar(.1,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.15,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.2,'%input','%output');quit;&amp;quot; &lt;br /&gt;
 matlab -r &amp;quot;foobar(.25,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.3,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
The different command lines to evaluate the performance of each parameter set over the whole database will be generated automatically from each line in the README file containing both '%input' and '%output' strings.&lt;br /&gt;
&lt;br /&gt;
== Evaluation Procedures ==&lt;br /&gt;
&lt;br /&gt;
For the evalution procedure we will use&lt;br /&gt;
*'''F-measure''' - the standard calculation as used in onset and beat tracking evaluation with a +/-70ms window, see Dixon (2007).&lt;br /&gt;
&lt;br /&gt;
Given the high diversity of musical styles included in the task, results will be reported per each invidiual dataset. &lt;br /&gt;
&lt;br /&gt;
== Time and hardware limits ==&lt;br /&gt;
Due to the potentially high number of participants in this and other audio tasks, hard limits on the runtime of submissions will be imposed.&lt;br /&gt;
&lt;br /&gt;
A hard limit of 24 hours will be imposed on analysis times. Submissions exceeding this limit may not receive a result.&lt;br /&gt;
&lt;br /&gt;
== Potential Participants ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
Jose R. Zapata / joser.zapata (at) upb.edu.co&lt;br /&gt;
&lt;br /&gt;
== Discussion ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
= Bibliography =&lt;br /&gt;
&lt;br /&gt;
S. Dixon, F. Gouyon and G. Widmer, [http://ismir2004.ismir.net/proceedings/p093-page-509-paper165.pdf Towards Characterisation of Music via Rhythmic Patterns], In Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR 2004), pp 509-516.&lt;br /&gt;
&lt;br /&gt;
S. Dixon, [http://www.eecs.qmul.ac.uk/~simond/pub/2007/jnmr07.pdf Evaluation of audio beat tracking system BeatRoot], Journal of New Music Research, vol. 36, no. 1, pp. 39-51, 2007.&lt;br /&gt;
&lt;br /&gt;
J. A. Hockman, M. E. P. Davies, I. Fujinaga.[http://ismir2012.ismir.net/event/papers/169-ismir-2012.pdf ONE IN THE JUNGLE: Downbeat Detection in Hardcore, Jungle, and Drum and Bass], In Proceedings of 13th International Society for Music Information Retrieval Conference (ISMIR), Porto, Portugal pp. 169-174, 2012.&lt;br /&gt;
&lt;br /&gt;
F. Krebs, S. Boeck, and G. Widmer, [http://www.cp.jku.at/research/papers/Krebs_etal_ISMIR_2013.pdf Rhythmic Pattern Modeling for Beat- and Downbeat Tracking in Musical Audio], In Proceedings of 14th International Society for Music Information Retrieval Conference (ISMIR), Curitiba, Brazil, 2013.&lt;br /&gt;
&lt;br /&gt;
M. Mauch, C. Cannam, M. E. P. Davies, S. Dixon, C. Harte, S. Kolozali and D. Tidhar, [http://ismir2009.ismir.net/proceedings/LBD-18.pdf OMRAS2 Metadata Project 2009], Late-breaking session at the 10th International Conference on Music Information Retrieval, 2009.&lt;br /&gt;
&lt;br /&gt;
A. Srinivasamurthy, A. Holzapfel, and Xavier Serra, [http://www.tandfonline.com/doi/full/10.1080/09298215.2013.879902 In Search of Automatic Rhythm Analysis Methods for Turkish and Indian Art Music], Journal of New Music Research, vol. 43, no. 1, pp. 94-114, 2014.&lt;/div&gt;</summary>
		<author><name>Matthew Davies</name></author>
		
	</entry>
	<entry>
		<id>https://music-ir.org/mirex/w/index.php?title=MIREX_HOME&amp;diff=10074</id>
		<title>MIREX HOME</title>
		<link rel="alternate" type="text/html" href="https://music-ir.org/mirex/w/index.php?title=MIREX_HOME&amp;diff=10074"/>
		<updated>2014-05-27T15:21:04Z</updated>

		<summary type="html">&lt;p&gt;Matthew Davies: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;==Welcome to MIREX 2014==&lt;br /&gt;
&lt;br /&gt;
This is the main page for the tenth running of the Music Information Retrieval Evaluation eXchange (MIREX 2014). The International Music Information Retrieval Systems Evaluation Laboratory ([https://music-ir.org/evaluation IMIRSEL]) at the Graduate School of Library and Information Science ([http://www.lis.illinois.edu GSLIS]), University of Illinois at Urbana-Champaign ([http://www.illinois.edu UIUC]) is the principal organizer of MIREX 2014. &lt;br /&gt;
&lt;br /&gt;
The MIREX 2014 community will hold its annual meeting as part of [http://ismir2014.ismir.net/ The 15th International Conference on Music Information Retrieval], ISMIR 2014, which will be held in Taipei, Taiwan, the 27-31 October, 2014. The MIREX plenary and poster sessions will be held during the conference.&lt;br /&gt;
&lt;br /&gt;
J. Stephen Downie&amp;lt;br&amp;gt;&lt;br /&gt;
Director, IMIRSEL&amp;lt;br&amp;gt;&lt;br /&gt;
&lt;br /&gt;
==Task Leadership Model==&lt;br /&gt;
&lt;br /&gt;
Like ISMIR 2013, we are prepared to improve the distribution of tasks for the upcoming MIREX 2014.  To do so, we really need leaders to help us organize and run each task.&lt;br /&gt;
&lt;br /&gt;
To volunteer to lead a task, please add your name to the &amp;quot;Captains&amp;quot; column on the new [[2014:Task Captains]] page. Please direct any communication to the [https://mail.lis.illinois.edu/mailman/listinfo/evalfest EvalFest] mailing list.&lt;br /&gt;
&lt;br /&gt;
What does it mean to lead a task?&lt;br /&gt;
* Update wiki pages as needed&lt;br /&gt;
* Communicate with submitters and troubleshooting submissions&lt;br /&gt;
* Execution and evaluation of submissions&lt;br /&gt;
* Publishing final results&lt;br /&gt;
&lt;br /&gt;
Due to the proprietary nature of much of the data, the submission system, evaluation framework, and most of the datasets will continue to be hosted by IMIRSEL. However, we are prepared to provide access to task organizers to manage and run submissions on the IMIRSEL systems.&lt;br /&gt;
&lt;br /&gt;
We really need leaders to help us this year!&lt;br /&gt;
&lt;br /&gt;
==MIREX 2014 Deadline Dates==&lt;br /&gt;
&lt;br /&gt;
This year, we have different deadlines for different tasks:&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
* '''July 16th 2014'''&lt;br /&gt;
** Audio Classification (Train/Test) Tasks&lt;br /&gt;
** Audio K-POP Genre Classification&lt;br /&gt;
** Audio K-POP Mood Classification&lt;br /&gt;
** Audio Tag Classification&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
* '''August 16th 2014'''&lt;br /&gt;
** Audio Music Similarity and Retrieval&lt;br /&gt;
** Symbolic Melodic Similarity   &lt;br /&gt;
** Structural Segmentation&lt;br /&gt;
** Audio Tempo Estimation&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
* '''September 16th 2014'''&lt;br /&gt;
** Audio Onset Detection  &lt;br /&gt;
** Audio Beat Tracking     &lt;br /&gt;
** Audio Key Detection&lt;br /&gt;
** Multiple Fundamental Frequency Estimation &amp;amp; Tracking&lt;br /&gt;
** Real-time Audio to Score Alignment (a.k.a Score Following)&lt;br /&gt;
** Audio Cover Song Identification&lt;br /&gt;
** Discovery of Repeated Themes &amp;amp; Sections&lt;br /&gt;
** Audio Melody Extraction&lt;br /&gt;
** Query by Singing/Humming&lt;br /&gt;
** Query by Tapping&lt;br /&gt;
** Audio Chord Estimation&lt;br /&gt;
** Audio Downbeat Estimation&lt;br /&gt;
&lt;br /&gt;
	&lt;br /&gt;
&amp;lt;i&amp;gt;&amp;lt;b&amp;gt;Nota Bene:&amp;lt;/b&amp;gt; &amp;lt;/i&amp;gt;In the past we have been rather flexible about deadlines. This year, however, we simply do not have the time flexibility, sorry.&lt;br /&gt;
&lt;br /&gt;
Please, please, please, let's start getting those submissions made. The sooner we have the code, the sooner we can start running the evaluations.&lt;br /&gt;
&lt;br /&gt;
PS: If you have a slower running algorithm, help us help you by getting your code in ASAP. Please do pay attention to runtime limits.&lt;br /&gt;
&lt;br /&gt;
==MIREX 2014 Submission Instructions==&lt;br /&gt;
* Be sure to read through the rest of this page&lt;br /&gt;
* Be sure to read though the task pages for which you are submitting&lt;br /&gt;
* Be sure to follow the [[2009:Best Coding Practices for MIREX | Best Coding Practices for MIREX]]&lt;br /&gt;
* Be sure to follow the [[MIREX 2014 Submission Instructions]]including both the tutorial video and the text&lt;br /&gt;
&lt;br /&gt;
==MIREX 2014 Possible Evaluation Tasks==&lt;br /&gt;
&lt;br /&gt;
* [[2014:Audio Classification (Train/Test) Tasks]], incorporating:&lt;br /&gt;
** Audio US Pop Genre Classification&lt;br /&gt;
** Audio Latin Genre Classification&lt;br /&gt;
** Audio Music Mood Classification&lt;br /&gt;
** Audio Classical Composer Identification&lt;br /&gt;
* [[2014:Audio Cover Song Identification]]&lt;br /&gt;
* [[2014:Audio Tag Classification]] &lt;br /&gt;
* [[2014:Audio Music Similarity and Retrieval]]&lt;br /&gt;
* [[2014:Symbolic Melodic Similarity]]&lt;br /&gt;
* [[2014:Audio Onset Detection]]&lt;br /&gt;
* [[2014:Audio Key Detection]]&lt;br /&gt;
* [[2014:Real-time Audio to Score Alignment (a.k.a Score Following)]]&lt;br /&gt;
* [[2014:Query by Singing/Humming]]&lt;br /&gt;
* [[2014:Audio Melody Extraction]]&lt;br /&gt;
* [[2014:Multiple Fundamental Frequency Estimation &amp;amp; Tracking]]&lt;br /&gt;
* [[2014:Audio Chord Estimation]]&lt;br /&gt;
* [[2014:Query by Tapping]]&lt;br /&gt;
* [[2014:Audio Beat Tracking]]&lt;br /&gt;
* [[2014:Structural Segmentation]]&lt;br /&gt;
* [[2014:Audio Tempo Estimation]]&lt;br /&gt;
* [[2014:Discovery of Repeated Themes &amp;amp; Sections]]&lt;br /&gt;
* [[2014:Audio Downbeat Estimation]]&lt;br /&gt;
&lt;br /&gt;
===Note to New Participants===&lt;br /&gt;
Please take the time to read the following review articles that explain the history and structure of MIREX.&lt;br /&gt;
&lt;br /&gt;
Downie, J. Stephen (2008). The Music Information Retrieval Evaluation Exchange (2005-2007):&amp;lt;br&amp;gt;&lt;br /&gt;
A window into music information retrieval research.''Acoustical Science and Technology 29'' (4): 247-255. &amp;lt;br&amp;gt;&lt;br /&gt;
Available at: [http://dx.doi.org/10.1250/ast.29.247 http://dx.doi.org/10.1250/ast.29.247]&lt;br /&gt;
&lt;br /&gt;
Downie, J. Stephen, Andreas F. Ehmann, Mert Bay and M. Cameron Jones. (2010).&amp;lt;br&amp;gt;&lt;br /&gt;
The Music Information Retrieval Evaluation eXchange: Some Observations and Insights.&amp;lt;br&amp;gt;&lt;br /&gt;
''Advances in Music Information Retrieval'' Vol. 274, pp. 93-115&amp;lt;br&amp;gt;&lt;br /&gt;
Available at: [http://bit.ly/KpM5u5 http://bit.ly/KpM5u5]&lt;br /&gt;
&lt;br /&gt;
===Note to All Participants===&lt;br /&gt;
Because MIREX is premised upon the sharing of ideas and results, '''ALL''' MIREX participants are expected to:&lt;br /&gt;
&lt;br /&gt;
# submit a DRAFT 2-3 page extended abstract PDF in the ISMIR format about the submitted programme(s) to help us and the community better understand how the algorithm works when submitting their programme(s).&lt;br /&gt;
# submit a FINALIZED 2-3 page extended abstract PDF in the ISMIR format prior to ISMIR 2014 for posting on the respective results pages (sometimes the same abstract can be used for multiple submissions; in many cases the DRAFT and FINALIZED abstracts are the same)&lt;br /&gt;
# present a poster at the MIREX 2014 poster session at ISMIR 2014&lt;br /&gt;
&lt;br /&gt;
===Software Dependency Requests===&lt;br /&gt;
If you have not submitted to MIREX before or are unsure whether IMIRSEL/NEMA currently supports some of the software/architecture dependencies for your submission a [https://docs.google.com/spreadsheet/viewform?formkey=dFpmNF9PUGdvd1o1OHVhMkZ4cXZvdkE6MA#gid=0 dependency request form is available]. Please submit details of your dependencies on this form and the IMIRSEL team will attempt to satisfy them for you. &lt;br /&gt;
&lt;br /&gt;
Due to the high volume of submissions expected at MIREX 2014, submissions with difficult to satisfy dependencies that the team has not been given sufficient notice of may result in the submission being rejected.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
Finally, you will also be expected to detail your software/architecture dependencies in a README file to be provided to the submission system.&lt;br /&gt;
&lt;br /&gt;
==Getting Involved in MIREX 2014==&lt;br /&gt;
MIREX is a community-based endeavour. Be a part of the community and help make MIREX 2014 the best yet.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
===Mailing List Participation===&lt;br /&gt;
If you are interested in formal MIR evaluation, you should also subscribe to the &amp;quot;MIREX&amp;quot; (aka &amp;quot;EvalFest&amp;quot;) mail list and participate in the community discussions about defining and running MIREX 2014 tasks. Subscription information at: &lt;br /&gt;
[https://mail.lis.illinois.edu/mailman/listinfo/evalfest EvalFest Central]. &lt;br /&gt;
&lt;br /&gt;
If you are participating in MIREX 2014, it is VERY IMPORTANT that you are subscribed to EvalFest. Deadlines, task updates and other important information will be announced via this mailing list. Please use the EvalFest for discussion of MIREX task proposals and other MIREX related issues. This wiki (MIREX 2014 wiki) will be used to embody and disseminate task proposals, however, task related discussions should be conducted on the MIREX organization mailing list (EvalFest) rather than on this wiki, but should be summarized here. &lt;br /&gt;
&lt;br /&gt;
Where possible, definitions or example code for new evaluation metrics or tasks should be provided to the IMIRSEL team who will embody them in software as part of the NEMA analytics framework, which will be released to the community at or before ISMIR 2014 - providing a standardised set of interfaces and output to disciplined evaluation procedures for a great many MIR tasks.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
===Wiki Participation===&lt;br /&gt;
If you find that you cannot edit a MIREX wiki page, you will need to create a new account via: [[Special:Userlogin]].&lt;br /&gt;
&lt;br /&gt;
Please note that because of &amp;quot;spam-bots&amp;quot;, MIREX wiki registration requests may be moderated by IMIRSEL members. It might take up to 24 hours for approval (Thank you for your patience!).&lt;br /&gt;
&lt;br /&gt;
==MIREX 2005 - 2013 Wikis==&lt;br /&gt;
Content from MIREX 2005 - 2013 are available at:&lt;br /&gt;
'''[[2013:Main_Page|MIREX 2013]]'''&lt;br /&gt;
'''[[2012:Main_Page|MIREX 2012]]''' &lt;br /&gt;
'''[[2011:Main_Page|MIREX 2011]]''' &lt;br /&gt;
'''[[2010:Main_Page|MIREX 2010]]''' &lt;br /&gt;
'''[[2009:Main_Page|MIREX 2009]]''' &lt;br /&gt;
'''[[2008:Main_Page|MIREX 2008]]''' &lt;br /&gt;
'''[[2007:Main_Page|MIREX 2007]]''' &lt;br /&gt;
'''[[2006:Main_Page|MIREX 2006]]''' &lt;br /&gt;
'''[[2005:Main_Page|MIREX 2005]]'''&lt;/div&gt;</summary>
		<author><name>Matthew Davies</name></author>
		
	</entry>
	<entry>
		<id>https://music-ir.org/mirex/w/index.php?title=2014:Task_Captains&amp;diff=10073</id>
		<title>2014:Task Captains</title>
		<link rel="alternate" type="text/html" href="https://music-ir.org/mirex/w/index.php?title=2014:Task_Captains&amp;diff=10073"/>
		<updated>2014-05-27T14:38:33Z</updated>

		<summary type="html">&lt;p&gt;Matthew Davies: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;Like ISMIR 2013, we are prepared to improve the distribution of tasks for the upcoming MIREX 2014.  To do so, we really need leaders to help us organize and run each task.&lt;br /&gt;
&lt;br /&gt;
To volunteer to lead one or more tasks, please add your name in the &amp;quot;Captains&amp;quot; column.&lt;br /&gt;
&lt;br /&gt;
What does it mean to lead a task?&lt;br /&gt;
* Update wiki pages as needed&lt;br /&gt;
* Communicate with submitters and troubleshooting submissions&lt;br /&gt;
* Execution and evaluation of submissions&lt;br /&gt;
* Publishing final results&lt;br /&gt;
&lt;br /&gt;
Due to the proprietary nature of much of the data, the submission system, evaluation framework, and most of the datasets will continue to be hosted by IMIRSEL. However, we are prepared to provide access to task organizers to manage and run submissions on the IMIRSEL systems.&lt;br /&gt;
 &lt;br /&gt;
&lt;br /&gt;
{| class=&amp;quot;wikitable&amp;quot; style=&amp;quot;margin-left: 20px&amp;quot;&lt;br /&gt;
!ID !! Task !! Captain(s)&lt;br /&gt;
|-&lt;br /&gt;
|abt&lt;br /&gt;
|[[2014:Audio Beat Tracking]]&lt;br /&gt;
|Fu-Hai Frank Wu&lt;br /&gt;
|-&lt;br /&gt;
|ace&lt;br /&gt;
|[[2014:Audio Chord Estimation]]&lt;br /&gt;
|Johan Pauwels&lt;br /&gt;
|-&lt;br /&gt;
|act&lt;br /&gt;
|[[2014:Audio Classification (Train/Test) Tasks]]&lt;br /&gt;
|&lt;br /&gt;
|-&lt;br /&gt;
|acs&lt;br /&gt;
|[[2014:Audio Cover Song Identification]]&lt;br /&gt;
|&lt;br /&gt;
|-&lt;br /&gt;
|ade&lt;br /&gt;
|[[2014:Audio Downbeat Estimation]]&lt;br /&gt;
|Matthew Davies, Sebastian Böck, Florian Krebs&lt;br /&gt;
|-&lt;br /&gt;
|akd&lt;br /&gt;
|[[2014:Audio Key Detection]]&lt;br /&gt;
|Johan Pauwels&lt;br /&gt;
|-&lt;br /&gt;
|ame&lt;br /&gt;
|[[2014:Audio Melody Extraction]]&lt;br /&gt;
|KETI&lt;br /&gt;
|-&lt;br /&gt;
|ams&lt;br /&gt;
|[[2014:Audio Music Similarity and Retrieval]]&lt;br /&gt;
|&lt;br /&gt;
|-&lt;br /&gt;
|aod&lt;br /&gt;
|[[2014:Audio Onset Detection]]&lt;br /&gt;
|Sebastian Böck&lt;br /&gt;
|-&lt;br /&gt;
|ate&lt;br /&gt;
|[[2014:Audio Tempo Estimation]]&lt;br /&gt;
|Aggelos Gkiokas&lt;br /&gt;
|-&lt;br /&gt;
|atg&lt;br /&gt;
|[[2014:Audio Tag Classification]]&lt;br /&gt;
|&lt;br /&gt;
|-&lt;br /&gt;
|mf0&lt;br /&gt;
|[[2014:Multiple Fundamental Frequency Estimation &amp;amp; Tracking]]&lt;br /&gt;
|&lt;br /&gt;
|-&lt;br /&gt;
|qbsh&lt;br /&gt;
|[[2014:Query by Singing/Humming]]&lt;br /&gt;
|KETI&lt;br /&gt;
|-&lt;br /&gt;
|qbt&lt;br /&gt;
|[[2014:Query by Tapping]]&lt;br /&gt;
| CCRMA&lt;br /&gt;
|-&lt;br /&gt;
|scofo&lt;br /&gt;
|[[2014:Real-time Audio to Score Alignment (a.k.a Score Following)]]&lt;br /&gt;
|&lt;br /&gt;
|-&lt;br /&gt;
|sms&lt;br /&gt;
|[[2014:Symbolic Melodic Similarity]]&lt;br /&gt;
|&lt;br /&gt;
|-&lt;br /&gt;
|struct&lt;br /&gt;
|[[2014:Structural Segmentation]]&lt;br /&gt;
|&lt;br /&gt;
|-&lt;br /&gt;
|drts&lt;br /&gt;
|[[2014:Discovery of Repeated Themes &amp;amp; Sections]]&lt;br /&gt;
|Tom Collins&lt;br /&gt;
|-&lt;br /&gt;
|kgc&lt;br /&gt;
|[[2014:Audio K-POP Genre Classification]]&lt;br /&gt;
|IMIRSEL (Kahyun Choi, Peter Organisciak)&lt;br /&gt;
|-&lt;br /&gt;
|kmc&lt;br /&gt;
|[[2014:Audio K-POP Mood Classification]]&lt;br /&gt;
|IMIRSEL (Kahyun Choi, Peter Organisciak)&lt;br /&gt;
|}&lt;/div&gt;</summary>
		<author><name>Matthew Davies</name></author>
		
	</entry>
	<entry>
		<id>https://music-ir.org/mirex/w/index.php?title=2014:Task_Captains&amp;diff=10072</id>
		<title>2014:Task Captains</title>
		<link rel="alternate" type="text/html" href="https://music-ir.org/mirex/w/index.php?title=2014:Task_Captains&amp;diff=10072"/>
		<updated>2014-05-27T14:28:22Z</updated>

		<summary type="html">&lt;p&gt;Matthew Davies: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;Like ISMIR 2013, we are prepared to improve the distribution of tasks for the upcoming MIREX 2014.  To do so, we really need leaders to help us organize and run each task.&lt;br /&gt;
&lt;br /&gt;
To volunteer to lead one or more tasks, please add your name in the &amp;quot;Captains&amp;quot; column.&lt;br /&gt;
&lt;br /&gt;
What does it mean to lead a task?&lt;br /&gt;
* Update wiki pages as needed&lt;br /&gt;
* Communicate with submitters and troubleshooting submissions&lt;br /&gt;
* Execution and evaluation of submissions&lt;br /&gt;
* Publishing final results&lt;br /&gt;
&lt;br /&gt;
Due to the proprietary nature of much of the data, the submission system, evaluation framework, and most of the datasets will continue to be hosted by IMIRSEL. However, we are prepared to provide access to task organizers to manage and run submissions on the IMIRSEL systems.&lt;br /&gt;
 &lt;br /&gt;
&lt;br /&gt;
{| class=&amp;quot;wikitable&amp;quot; style=&amp;quot;margin-left: 20px&amp;quot;&lt;br /&gt;
!ID !! Task !! Captain(s)&lt;br /&gt;
|-&lt;br /&gt;
|abt&lt;br /&gt;
|[[2014:Audio Beat Tracking]]&lt;br /&gt;
|Fu-Hai Frank Wu&lt;br /&gt;
|-&lt;br /&gt;
|ace&lt;br /&gt;
|[[2014:Audio Chord Estimation]]&lt;br /&gt;
|Johan Pauwels&lt;br /&gt;
|-&lt;br /&gt;
|act&lt;br /&gt;
|[[2014:Audio Classification (Train/Test) Tasks]]&lt;br /&gt;
|&lt;br /&gt;
|-&lt;br /&gt;
|acs&lt;br /&gt;
|[[2014:Audio Cover Song Identification]]&lt;br /&gt;
|&lt;br /&gt;
|-&lt;br /&gt;
|ade&lt;br /&gt;
|[[2014:Audio Downbeat Estimation]]&lt;br /&gt;
|Matthew Davies and Sebastian Böck&lt;br /&gt;
|-&lt;br /&gt;
|akd&lt;br /&gt;
|[[2014:Audio Key Detection]]&lt;br /&gt;
|Johan Pauwels&lt;br /&gt;
|-&lt;br /&gt;
|ame&lt;br /&gt;
|[[2014:Audio Melody Extraction]]&lt;br /&gt;
|KETI&lt;br /&gt;
|-&lt;br /&gt;
|ams&lt;br /&gt;
|[[2014:Audio Music Similarity and Retrieval]]&lt;br /&gt;
|&lt;br /&gt;
|-&lt;br /&gt;
|aod&lt;br /&gt;
|[[2014:Audio Onset Detection]]&lt;br /&gt;
|Sebastian Böck&lt;br /&gt;
|-&lt;br /&gt;
|ate&lt;br /&gt;
|[[2014:Audio Tempo Estimation]]&lt;br /&gt;
|Aggelos Gkiokas&lt;br /&gt;
|-&lt;br /&gt;
|atg&lt;br /&gt;
|[[2014:Audio Tag Classification]]&lt;br /&gt;
|&lt;br /&gt;
|-&lt;br /&gt;
|mf0&lt;br /&gt;
|[[2014:Multiple Fundamental Frequency Estimation &amp;amp; Tracking]]&lt;br /&gt;
|&lt;br /&gt;
|-&lt;br /&gt;
|qbsh&lt;br /&gt;
|[[2014:Query by Singing/Humming]]&lt;br /&gt;
|KETI&lt;br /&gt;
|-&lt;br /&gt;
|qbt&lt;br /&gt;
|[[2014:Query by Tapping]]&lt;br /&gt;
| CCRMA&lt;br /&gt;
|-&lt;br /&gt;
|scofo&lt;br /&gt;
|[[2014:Real-time Audio to Score Alignment (a.k.a Score Following)]]&lt;br /&gt;
|&lt;br /&gt;
|-&lt;br /&gt;
|sms&lt;br /&gt;
|[[2014:Symbolic Melodic Similarity]]&lt;br /&gt;
|&lt;br /&gt;
|-&lt;br /&gt;
|struct&lt;br /&gt;
|[[2014:Structural Segmentation]]&lt;br /&gt;
|&lt;br /&gt;
|-&lt;br /&gt;
|drts&lt;br /&gt;
|[[2014:Discovery of Repeated Themes &amp;amp; Sections]]&lt;br /&gt;
|Tom Collins&lt;br /&gt;
|-&lt;br /&gt;
|kgc&lt;br /&gt;
|[[2014:Audio K-POP Genre Classification]]&lt;br /&gt;
|IMIRSEL (Kahyun Choi, Peter Organisciak)&lt;br /&gt;
|-&lt;br /&gt;
|kmc&lt;br /&gt;
|[[2014:Audio K-POP Mood Classification]]&lt;br /&gt;
|IMIRSEL (Kahyun Choi, Peter Organisciak)&lt;br /&gt;
|}&lt;/div&gt;</summary>
		<author><name>Matthew Davies</name></author>
		
	</entry>
	<entry>
		<id>https://music-ir.org/mirex/w/index.php?title=2014:Task_Captains&amp;diff=10071</id>
		<title>2014:Task Captains</title>
		<link rel="alternate" type="text/html" href="https://music-ir.org/mirex/w/index.php?title=2014:Task_Captains&amp;diff=10071"/>
		<updated>2014-05-27T13:24:05Z</updated>

		<summary type="html">&lt;p&gt;Matthew Davies: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;Like ISMIR 2013, we are prepared to improve the distribution of tasks for the upcoming MIREX 2014.  To do so, we really need leaders to help us organize and run each task.&lt;br /&gt;
&lt;br /&gt;
To volunteer to lead one or more tasks, please add your name in the &amp;quot;Captains&amp;quot; column.&lt;br /&gt;
&lt;br /&gt;
What does it mean to lead a task?&lt;br /&gt;
* Update wiki pages as needed&lt;br /&gt;
* Communicate with submitters and troubleshooting submissions&lt;br /&gt;
* Execution and evaluation of submissions&lt;br /&gt;
* Publishing final results&lt;br /&gt;
&lt;br /&gt;
Due to the proprietary nature of much of the data, the submission system, evaluation framework, and most of the datasets will continue to be hosted by IMIRSEL. However, we are prepared to provide access to task organizers to manage and run submissions on the IMIRSEL systems.&lt;br /&gt;
 &lt;br /&gt;
&lt;br /&gt;
{| class=&amp;quot;wikitable&amp;quot; style=&amp;quot;margin-left: 20px&amp;quot;&lt;br /&gt;
!ID !! Task !! Captain(s)&lt;br /&gt;
|-&lt;br /&gt;
|abt&lt;br /&gt;
|[[2014:Audio Beat Tracking]]&lt;br /&gt;
|Fu-Hai Frank Wu&lt;br /&gt;
|-&lt;br /&gt;
|ace&lt;br /&gt;
|[[2014:Audio Chord Estimation]]&lt;br /&gt;
|Johan Pauwels&lt;br /&gt;
|-&lt;br /&gt;
|act&lt;br /&gt;
|[[2014:Audio Classification (Train/Test) Tasks]]&lt;br /&gt;
|&lt;br /&gt;
|-&lt;br /&gt;
|acs&lt;br /&gt;
|[[2014:Audio Cover Song Identification]]&lt;br /&gt;
|&lt;br /&gt;
|-&lt;br /&gt;
|ade&lt;br /&gt;
|[[2014:Audio Downbeat Estimation]]&lt;br /&gt;
|Matthew Davies&lt;br /&gt;
|-&lt;br /&gt;
|akd&lt;br /&gt;
|[[2014:Audio Key Detection]]&lt;br /&gt;
|Johan Pauwels&lt;br /&gt;
|-&lt;br /&gt;
|ame&lt;br /&gt;
|[[2014:Audio Melody Extraction]]&lt;br /&gt;
|KETI&lt;br /&gt;
|-&lt;br /&gt;
|ams&lt;br /&gt;
|[[2014:Audio Music Similarity and Retrieval]]&lt;br /&gt;
|&lt;br /&gt;
|-&lt;br /&gt;
|aod&lt;br /&gt;
|[[2014:Audio Onset Detection]]&lt;br /&gt;
|Sebastian Böck&lt;br /&gt;
|-&lt;br /&gt;
|ate&lt;br /&gt;
|[[2014:Audio Tempo Estimation]]&lt;br /&gt;
|Aggelos Gkiokas&lt;br /&gt;
|-&lt;br /&gt;
|atg&lt;br /&gt;
|[[2014:Audio Tag Classification]]&lt;br /&gt;
|&lt;br /&gt;
|-&lt;br /&gt;
|mf0&lt;br /&gt;
|[[2014:Multiple Fundamental Frequency Estimation &amp;amp; Tracking]]&lt;br /&gt;
|&lt;br /&gt;
|-&lt;br /&gt;
|qbsh&lt;br /&gt;
|[[2014:Query by Singing/Humming]]&lt;br /&gt;
|KETI&lt;br /&gt;
|-&lt;br /&gt;
|qbt&lt;br /&gt;
|[[2014:Query by Tapping]]&lt;br /&gt;
| CCRMA&lt;br /&gt;
|-&lt;br /&gt;
|scofo&lt;br /&gt;
|[[2014:Real-time Audio to Score Alignment (a.k.a Score Following)]]&lt;br /&gt;
|&lt;br /&gt;
|-&lt;br /&gt;
|sms&lt;br /&gt;
|[[2014:Symbolic Melodic Similarity]]&lt;br /&gt;
|&lt;br /&gt;
|-&lt;br /&gt;
|struct&lt;br /&gt;
|[[2014:Structural Segmentation]]&lt;br /&gt;
|&lt;br /&gt;
|-&lt;br /&gt;
|drts&lt;br /&gt;
|[[2014:Discovery of Repeated Themes &amp;amp; Sections]]&lt;br /&gt;
|Tom Collins&lt;br /&gt;
|-&lt;br /&gt;
|kgc&lt;br /&gt;
|[[2014:Audio K-POP Genre Classification]]&lt;br /&gt;
|IMIRSEL (Kahyun Choi, Peter Organisciak)&lt;br /&gt;
|-&lt;br /&gt;
|kmc&lt;br /&gt;
|[[2014:Audio K-POP Mood Classification]]&lt;br /&gt;
|IMIRSEL (Kahyun Choi, Peter Organisciak)&lt;br /&gt;
|}&lt;/div&gt;</summary>
		<author><name>Matthew Davies</name></author>
		
	</entry>
	<entry>
		<id>https://music-ir.org/mirex/w/index.php?title=2014:Main_Page&amp;diff=10070</id>
		<title>2014:Main Page</title>
		<link rel="alternate" type="text/html" href="https://music-ir.org/mirex/w/index.php?title=2014:Main_Page&amp;diff=10070"/>
		<updated>2014-05-27T13:06:43Z</updated>

		<summary type="html">&lt;p&gt;Matthew Davies: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;==Welcome to MIREX 2014==&lt;br /&gt;
&lt;br /&gt;
This is the main page for the tenth running of the Music Information Retrieval Evaluation eXchange (MIREX 2014). The International Music Information Retrieval Systems Evaluation Laboratory ([https://music-ir.org/evaluation IMIRSEL]) at the Graduate School of Library and Information Science ([http://www.lis.illinois.edu GSLIS]), University of Illinois at Urbana-Champaign ([http://www.illinois.edu UIUC]) is the principal organizer of MIREX 2014. &lt;br /&gt;
&lt;br /&gt;
The MIREX 2014 community will hold its annual meeting as part of [http://ismir2014.ismir.net/ The 15th International Conference on Music Information Retrieval], ISMIR 2014, which will be held in Taipei, Taiwan, the 27-31 October, 2014. The MIREX plenary and poster sessions will be held during the conference.&lt;br /&gt;
&lt;br /&gt;
J. Stephen Downie&amp;lt;br&amp;gt;&lt;br /&gt;
Director, IMIRSEL&amp;lt;br&amp;gt;&lt;br /&gt;
&lt;br /&gt;
==Task Leadership Model==&lt;br /&gt;
&lt;br /&gt;
Like ISMIR 2013, we are prepared to improve the distribution of tasks for the upcoming MIREX 2014.  To do so, we really need leaders to help us organize and run each task.&lt;br /&gt;
&lt;br /&gt;
To volunteer to lead a task, please add your name to the &amp;quot;Captains&amp;quot; column on the new [[2014:Task Captains]] page. Please direct any communication to the [https://mail.lis.illinois.edu/mailman/listinfo/evalfest EvalFest] mailing list.&lt;br /&gt;
&lt;br /&gt;
What does it mean to lead a task?&lt;br /&gt;
* Update wiki pages as needed&lt;br /&gt;
* Communicate with submitters and troubleshooting submissions&lt;br /&gt;
* Execution and evaluation of submissions&lt;br /&gt;
* Publishing final results&lt;br /&gt;
&lt;br /&gt;
Due to the proprietary nature of much of the data, the submission system, evaluation framework, and most of the datasets will continue to be hosted by IMIRSEL. However, we are prepared to provide access to task organizers to manage and run submissions on the IMIRSEL systems.&lt;br /&gt;
&lt;br /&gt;
We really need leaders to help us this year!&lt;br /&gt;
&lt;br /&gt;
==MIREX 2014 Deadline Dates==&lt;br /&gt;
&lt;br /&gt;
This year, we have different deadlines for different tasks:&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
* '''July 16th 2014'''&lt;br /&gt;
** Audio Classification (Train/Test) Tasks&lt;br /&gt;
** Audio K-POP Genre Classification&lt;br /&gt;
** Audio K-POP Mood Classification&lt;br /&gt;
** Audio Tag Classification&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
* '''August 16th 2014'''&lt;br /&gt;
** Audio Music Similarity and Retrieval&lt;br /&gt;
** Symbolic Melodic Similarity   &lt;br /&gt;
** Structural Segmentation&lt;br /&gt;
** Audio Tempo Estimation&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
* '''September 16th 2014'''&lt;br /&gt;
** Audio Onset Detection  &lt;br /&gt;
** Audio Beat Tracking     &lt;br /&gt;
** Audio Key Detection&lt;br /&gt;
** Multiple Fundamental Frequency Estimation &amp;amp; Tracking&lt;br /&gt;
** Real-time Audio to Score Alignment (a.k.a Score Following)&lt;br /&gt;
** Audio Cover Song Identification&lt;br /&gt;
** Discovery of Repeated Themes &amp;amp; Sections&lt;br /&gt;
** Audio Melody Extraction&lt;br /&gt;
** Query by Singing/Humming&lt;br /&gt;
** Query by Tapping&lt;br /&gt;
** Audio Chord Estimation&lt;br /&gt;
** Audio Downbeat Estimation     &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&amp;lt;i&amp;gt;&amp;lt;b&amp;gt;Nota Bene:&amp;lt;/b&amp;gt; &amp;lt;/i&amp;gt;In the past we have been rather flexible about deadlines. This year, however, we simply do not have the time flexibility, sorry.&lt;br /&gt;
&lt;br /&gt;
Please, please, please, let's start getting those submissions made. The sooner we have the code, the sooner we can start running the evaluations.&lt;br /&gt;
&lt;br /&gt;
PS: If you have a slower running algorithm, help us help you by getting your code in ASAP. Please do pay attention to runtime limits.&lt;br /&gt;
&lt;br /&gt;
==MIREX 2014 Submission Instructions==&lt;br /&gt;
* Be sure to read through the rest of this page&lt;br /&gt;
* Be sure to read though the task pages for which you are submitting&lt;br /&gt;
* Be sure to follow the [[2009:Best Coding Practices for MIREX | Best Coding Practices for MIREX]]&lt;br /&gt;
* Be sure to follow the  [[MIREX 2014 Submission Instructions]] including both the tutorial video and the text&lt;br /&gt;
&lt;br /&gt;
==MIREX 2014 Possible Evaluation Tasks==&lt;br /&gt;
&lt;br /&gt;
* [[2014:Audio Classification (Train/Test) Tasks]], incorporating:&lt;br /&gt;
** Audio US Pop Genre Classification&lt;br /&gt;
** Audio Latin Genre Classification&lt;br /&gt;
** Audio Music Mood Classification&lt;br /&gt;
** Audio Classical Composer Identification&lt;br /&gt;
* [[2014:Audio_K-POP_Genre_Classification]]&lt;br /&gt;
* [[2014:Audio_K-POP_Mood_Classification]]&lt;br /&gt;
* [[2014:Audio Cover Song Identification]]&lt;br /&gt;
* [[2014:Audio Tag Classification]] &lt;br /&gt;
* [[2014:Audio Music Similarity and Retrieval]]&lt;br /&gt;
* [[2014:Symbolic Melodic Similarity]]&lt;br /&gt;
* [[2014:Audio Onset Detection]]&lt;br /&gt;
* [[2014:Audio Key Detection]]&lt;br /&gt;
* [[2014:Real-time Audio to Score Alignment (a.k.a Score Following)]]&lt;br /&gt;
* [[2014:Query by Singing/Humming]]&lt;br /&gt;
* [[2014:Audio Melody Extraction]]&lt;br /&gt;
* [[2014:Multiple Fundamental Frequency Estimation &amp;amp; Tracking]]&lt;br /&gt;
* [[2014:Audio Chord Estimation]]&lt;br /&gt;
* [[2014:Query by Tapping]]&lt;br /&gt;
* [[2014:Audio Beat Tracking]]&lt;br /&gt;
* [[2014:Structural Segmentation]]&lt;br /&gt;
* [[2014:Audio Tempo Estimation]]&lt;br /&gt;
* [[2014:Discovery of Repeated Themes &amp;amp; Sections]]&lt;br /&gt;
* [[2014:Audio Downbeat Estimation]]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
===Note to New Participants===&lt;br /&gt;
Please take the time to read the following review articles that explain the history and structure of MIREX.&lt;br /&gt;
&lt;br /&gt;
Downie, J. Stephen (2008). The Music Information Retrieval Evaluation Exchange (2005-2007):&amp;lt;br&amp;gt;&lt;br /&gt;
A window into music information retrieval research.''Acoustical Science and Technology 29'' (4): 247-255. &amp;lt;br&amp;gt;&lt;br /&gt;
Available at: [http://dx.doi.org/10.1250/ast.29.247 http://dx.doi.org/10.1250/ast.29.247]&lt;br /&gt;
&lt;br /&gt;
Downie, J. Stephen, Andreas F. Ehmann, Mert Bay and M. Cameron Jones. (2010).&amp;lt;br&amp;gt;&lt;br /&gt;
The Music Information Retrieval Evaluation eXchange: Some Observations and Insights.&amp;lt;br&amp;gt;&lt;br /&gt;
''Advances in Music Information Retrieval'' Vol. 274, pp. 93-115&amp;lt;br&amp;gt;&lt;br /&gt;
Available at: [http://bit.ly/KpM5u5 http://bit.ly/KpM5u5]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
===Runtime Limits===&lt;br /&gt;
&lt;br /&gt;
We reserve the right to stop any process that exceeds runtime limits for each task.  We will do our best to notify you in enough time to allow revisions, but this may not be possible in some cases. Please respect the published runtime limits.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
===Note to All Participants===&lt;br /&gt;
&lt;br /&gt;
Because MIREX is premised upon the sharing of ideas and results, '''ALL''' MIREX participants are expected to:&lt;br /&gt;
&lt;br /&gt;
# submit a DRAFT 2-3 page extended abstract PDF in the ISMIR format about the submitted programme(s) to help us and the community better understand how the algorithm works when submitting their programme(s).&lt;br /&gt;
# submit a FINALIZED 2-3 page extended abstract PDF in the ISMIR format prior to ISMIR 2014 for posting on the respective results pages (sometimes the same abstract can be used for multiple submissions; in many cases the DRAFT and FINALIZED abstracts are the same)&lt;br /&gt;
# present a poster at the MIREX 2014 poster session at ISMIR 2014&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
===Software Dependency Requests===&lt;br /&gt;
If you have not submitted to MIREX before or are unsure whether IMIRSEL currently supports some of the software/architecture dependencies for your submission a [https://docs.google.com/spreadsheet/viewform?formkey=dFpmNF9PUGdvd1o1OHVhMkZ4cXZvdkE6MA#gid=0 dependency request form is available]. Please submit details of your dependencies on this form and the IMIRSEL team will attempt to satisfy them for you. &lt;br /&gt;
&lt;br /&gt;
Due to the high volume of submissions expected at MIREX 2014, submissions with difficult to satisfy dependencies that the team has not been given sufficient notice of may result in the submission being rejected.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
Finally, you will also be expected to detail your software/architecture dependencies in a README file to be provided to the submission system.&lt;br /&gt;
&lt;br /&gt;
==Getting Involved in MIREX 2014==&lt;br /&gt;
MIREX is a community-based endeavour. Be a part of the community and help make MIREX 2014 the best yet.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
===Mailing List Participation===&lt;br /&gt;
If you are interested in formal MIR evaluation, you should also subscribe to the &amp;quot;MIREX&amp;quot; (aka &amp;quot;EvalFest&amp;quot;) mail list and participate in the community discussions about defining and running MIREX 2014 tasks. Subscription information at: &lt;br /&gt;
[https://mail.lis.illinois.edu/mailman/listinfo/evalfest EvalFest Central]. &lt;br /&gt;
&lt;br /&gt;
If you are participating in MIREX 2014, it is VERY IMPORTANT that you are subscribed to EvalFest. Deadlines, task updates and other important information will be announced via this mailing list. Please use the EvalFest for discussion of MIREX task proposals and other MIREX related issues. This wiki (MIREX 2014 wiki) will be used to embody and disseminate task proposals, however, task related discussions should be conducted on the MIREX organization mailing list (EvalFest) rather than on this wiki, but should be summarized here. &lt;br /&gt;
&lt;br /&gt;
Where possible, definitions or example code for new evaluation metrics or tasks should be provided to the IMIRSEL team who will embody them in software as part of the NEMA analytics framework, which will be released to the community at or before ISMIR 2014 - providing a standardised set of interfaces and output to disciplined evaluation procedures for a great many MIR tasks.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
===Wiki Participation===&lt;br /&gt;
If you find that you cannot edit a MIREX wiki page, you will need to create a new account via: [[Special:Userlogin]].&lt;br /&gt;
&lt;br /&gt;
Please note that because of &amp;quot;spam-bots&amp;quot;, MIREX wiki registration requests may be moderated by IMIRSEL members. It might take up to 24 hours for approval (Thank you for your patience!).&lt;br /&gt;
&lt;br /&gt;
==MIREX 2005 - 2013 Wikis==&lt;br /&gt;
Content from MIREX 2005 - 2013 are available at:&lt;br /&gt;
'''[[2013:Main_Page|MIREX 2013]]''' &lt;br /&gt;
'''[[2012:Main_Page|MIREX 2012]]''' &lt;br /&gt;
'''[[2011:Main_Page|MIREX 2011]]''' &lt;br /&gt;
'''[[2010:Main_Page|MIREX 2010]]''' &lt;br /&gt;
'''[[2009:Main_Page|MIREX 2009]]''' &lt;br /&gt;
'''[[2008:Main_Page|MIREX 2008]]''' &lt;br /&gt;
'''[[2007:Main_Page|MIREX 2007]]''' &lt;br /&gt;
'''[[2006:Main_Page|MIREX 2006]]''' &lt;br /&gt;
'''[[2005:Main_Page|MIREX 2005]]'''&lt;/div&gt;</summary>
		<author><name>Matthew Davies</name></author>
		
	</entry>
	<entry>
		<id>https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10069</id>
		<title>2014:Audio Downbeat Estimation</title>
		<link rel="alternate" type="text/html" href="https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10069"/>
		<updated>2014-05-26T15:53:54Z</updated>

		<summary type="html">&lt;p&gt;Matthew Davies: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Description ==&lt;br /&gt;
&lt;br /&gt;
'''This task is new for 2014!'''&lt;br /&gt;
&lt;br /&gt;
This text has been adapted from the Audio Beat Tracking Wiki page.  Please add your comments and discussion at the bottom of this page.&lt;br /&gt;
&lt;br /&gt;
The aim of the automatic downbeat estimation task is to identify the locations of downbeats in a collection of sound files. While this is similar to the Audio Beat Tracking task, here the aim is to find the first beat of each bar (measure) rather than all beat times. Algorithms are '''not''' required to estimate beat times or time-signature in addition to downbeats.&lt;br /&gt;
&lt;br /&gt;
Submitted algorithms will be evaluated in terms of their accuracy in finding downbeat locations (only) as annotated by musical experts across several diverse datasets.&lt;br /&gt;
&lt;br /&gt;
== Data ==&lt;br /&gt;
&lt;br /&gt;
=== Collections ===&lt;br /&gt;
'''Ballroom'''&lt;br /&gt;
The ballroom dataset contains eight different dance styles (Cha Cha, Jive, Quickstep, Rumba, Samba, Tango, Viennese Waltz and Waltz). It consists of '''697''' excerpts of 30s in duration.&lt;br /&gt;
The dataset contains two different meters 3/4 and 4/4 - but all pieces have constant meter. For further information see Dixon et al (2004) and Krebs et al (2013).&lt;br /&gt;
Note, we are using the ground truth annotations from Krebs et al. (2013) available at https://github.com/CPJKU/BallroomAnnotations&lt;br /&gt;
&lt;br /&gt;
'''Isophonics (Beatles only)'''&lt;br /&gt;
The Beatles dataset from the Centre for Digital Music at Queen Mary, University of London (http://www.isophonics.net/), as also used for Audio Chord Estimation in MIREX for many years. &lt;br /&gt;
This dataset contains '''179''' complete songs (all except Revolution 9), the majority of which are in 4/4.&lt;br /&gt;
For further information see Mauch et al (2009).&lt;br /&gt;
&lt;br /&gt;
'''Turkish Data'''&lt;br /&gt;
The Turkish corpus is an extended version of the annotated data used in Srinivasamurthy et al. (2014). It includes '''82''' excerpts of one&lt;br /&gt;
minute length each, and each piece belongs to one of three&lt;br /&gt;
rhythm classes that are referred to as usul in Turkish Art&lt;br /&gt;
music. 32 pieces are in the 9/8-usul Aksak, 20 pieces&lt;br /&gt;
in the 10/8-usul Curcuna, and 30 samples in the 8/8-usul&lt;br /&gt;
Düyek.&lt;br /&gt;
&lt;br /&gt;
'''Cretan Data'''&lt;br /&gt;
The corpus of Cretan music consists of '''42''' full length pieces of Cretan leaping dances. While there are several dances that differ in terms of their steps, the differences in&lt;br /&gt;
the sound are most noticeable in the melodic content, and all pieces can be considered to belong to one rhythmic style. All these dances are usually notated using a 2/4 time signature,&lt;br /&gt;
and the accompanying rhythmical patterns are usually played on a Cretan lute. While a variety of rhythmic patterns exist, they do not relate to a specific dance and can be&lt;br /&gt;
assumed to occur in all of the 42 songs in this corpus.&lt;br /&gt;
&lt;br /&gt;
'''Carnatic Data'''&lt;br /&gt;
The Carnatic music dataset is a subset of the CompMusic [http://compmusic.upf.edu/carnatic-rhythm-dataset Carnatic Music Rhythm Dataset]. It includes '''118''' two minute long excerpts spanning four most commonly used tālas (the rhythmic framework of Carnatic music, consisting of time cycles) of Carnatic music. There are 30 examples in each of ādi tāla (8 beats/cycle), rūpaka tāla (3 beats/cycle) and miśra chāpu tāla (7 beats/cycle), and 28 examples in khaṇḍa chāpu tāla (5 beats/cycle). The beats of the tāla in miśra chāpu and khaṇḍa chāpu are non-uniform, but for consistency with other datasets, a uniform beat pulse was obtained by interpolating the non-uniformly spaced beat locations. The recordings consist of both vocal and instrumental music recordings representative of the present day performance practice. All recordings contain percussion accompaniment, mainly the Mridangam. &lt;br /&gt;
&lt;br /&gt;
'''HJDB''' (to be confirmed)&lt;br /&gt;
The HJDB dataset contains '''236''' excerpts of Hardcore, Jungle and Drum and Bass music between 30s and 2 minutes in length. All excerpts are in 4/4 and have a constant tempo. &lt;br /&gt;
For further information see Hockman et al (2012).&lt;br /&gt;
&lt;br /&gt;
In total this makes '''1354''' excerpts (of which 259 are full length songs).&lt;br /&gt;
&lt;br /&gt;
=== Audio Formats ===&lt;br /&gt;
&lt;br /&gt;
The data are monophonic sound files&lt;br /&gt;
&lt;br /&gt;
* CD-quality (PCM, 16-bit, 44100 Hz) for all except Ballroom (originally lower quality, but resampled to 44100 Hz)&lt;br /&gt;
* single channel (mono)&lt;br /&gt;
&lt;br /&gt;
== Submission Format ==&lt;br /&gt;
Submissions to this task will have to conform to a specified format detailed below. Submissions should be packaged and contain at least two files: The algorithm itself and a README containing contact information and detailing, in full, the use of the algorithm.&lt;br /&gt;
&lt;br /&gt;
=== Input Data ===&lt;br /&gt;
Participating algorithms will have to read audio in the following format:&lt;br /&gt;
&lt;br /&gt;
* Sample rate: 44.1 KHz&lt;br /&gt;
* Sample size: 16 bit&lt;br /&gt;
* Number of channels: 1 (mono)&lt;br /&gt;
* Encoding: WAV &lt;br /&gt;
&lt;br /&gt;
=== Output Data ===&lt;br /&gt;
&lt;br /&gt;
The downbeat estimation algorithms will return downbeat times in an ASCII text file for each input .wav audio file. The specification of this output file is immediately below.&lt;br /&gt;
&lt;br /&gt;
=== Output File Format (Audio Downbeat Estimation) ===&lt;br /&gt;
&lt;br /&gt;
The downbeat output file format is an ASCII text format. Each downbeat time is specified, in seconds, on its own line. Specifically, &lt;br /&gt;
&lt;br /&gt;
 &amp;lt;downbeat time (in seconds)&amp;gt;\n&lt;br /&gt;
&lt;br /&gt;
where \n denotes the end of line. The &amp;lt; and &amp;gt; characters are not included. An example output file would look something like:&lt;br /&gt;
&lt;br /&gt;
 0.243&lt;br /&gt;
 1.486&lt;br /&gt;
 2.729&lt;br /&gt;
&lt;br /&gt;
=== Algorithm Calling Format ===&lt;br /&gt;
&lt;br /&gt;
The submitted algorithm must take as arguments a SINGLE .wav file to perform the downbeat estimation as well as the full output path and filename of the output file. The ability to specify the output path and file name is essential. Denoting the input .wav file path and name as %input and the output file path and name as %output, a program called foobar could be called from the command-line as follows:&lt;br /&gt;
&lt;br /&gt;
 foobar %input %output&lt;br /&gt;
 foobar -i %input -o %output&lt;br /&gt;
&lt;br /&gt;
Moreover, if your submission takes additional parameters, such as a detection threshold, foobar could be called like:&lt;br /&gt;
&lt;br /&gt;
 foobar .1 %input %output&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output  &lt;br /&gt;
&lt;br /&gt;
If your submission is in MATLAB, it should be submitted as a function. Once again, the function must contain String inputs for the full path and names of the input and output files. Parameters could also be specified as input arguments of the function. For example: &lt;br /&gt;
&lt;br /&gt;
 foobar('%input','%output')&lt;br /&gt;
 foobar(.1,'%input','%output')&lt;br /&gt;
&lt;br /&gt;
=== README File ===&lt;br /&gt;
&lt;br /&gt;
A README file accompanying each submission should contain explicit instructions on how to to run the program (as well as contact information, etc.). In particular, each command line to run should be specified, using %input for the input sound file and %output for the resulting text file.&lt;br /&gt;
&lt;br /&gt;
For instance, to test the program foobar with different values for parameters param1, the README file would look like:&lt;br /&gt;
&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output&lt;br /&gt;
 foobar -param1 .15 -i %input -o %output&lt;br /&gt;
 foobar -param1 .2 -i %input -o %output&lt;br /&gt;
 foobar -param1 .25 -i %input -o %output&lt;br /&gt;
 foobar -param1 .3 -i %input -o %output&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
For a submission using MATLAB, the README file could look like:&lt;br /&gt;
&lt;br /&gt;
 matlab -r &amp;quot;foobar(.1,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.15,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.2,'%input','%output');quit;&amp;quot; &lt;br /&gt;
 matlab -r &amp;quot;foobar(.25,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.3,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
The different command lines to evaluate the performance of each parameter set over the whole database will be generated automatically from each line in the README file containing both '%input' and '%output' strings.&lt;br /&gt;
&lt;br /&gt;
== Evaluation Procedures ==&lt;br /&gt;
&lt;br /&gt;
For the evalution procedure we will use&lt;br /&gt;
*'''F-measure''' - the standard calculation as used in onset and beat tracking evaluation with a +/-70ms window, see Dixon (2007).&lt;br /&gt;
&lt;br /&gt;
Given the high diversity of musical styles included in the task, results will be reported per each invidiual dataset. &lt;br /&gt;
&lt;br /&gt;
== Time and hardware limits ==&lt;br /&gt;
Due to the potentially high number of participants in this and other audio tasks, hard limits on the runtime of submissions will be imposed.&lt;br /&gt;
&lt;br /&gt;
A hard limit of 24 hours will be imposed on analysis times. Submissions exceeding this limit may not receive a result.&lt;br /&gt;
&lt;br /&gt;
== Potential Participants ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
== Discussion ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
= Bibliography =&lt;br /&gt;
&lt;br /&gt;
S. Dixon, F. Gouyon and G. Widmer, [http://ismir2004.ismir.net/proceedings/p093-page-509-paper165.pdf Towards Characterisation of Music via Rhythmic Patterns], In Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR 2004), pp 509-516.&lt;br /&gt;
&lt;br /&gt;
S. Dixon, [http://www.eecs.qmul.ac.uk/~simond/pub/2007/jnmr07.pdf Evaluation of audio beat tracking system BeatRoot], Journal of New Music Research, vol. 36, no. 1, pp. 39-51, 2007.&lt;br /&gt;
&lt;br /&gt;
J. A. Hockman, M. E. P. Davies, I. Fujinaga.[http://ismir2012.ismir.net/event/papers/169-ismir-2012.pdf ONE IN THE JUNGLE: Downbeat Detection in Hardcore, Jungle, and Drum and Bass], In Proceedings of 13th International Society for Music Information Retrieval Conference (ISMIR), Porto, Portugal pp. 169-174, 2012.&lt;br /&gt;
&lt;br /&gt;
F. Krebs, S. Boeck, and G. Widmer, [http://www.cp.jku.at/research/papers/Krebs_etal_ISMIR_2013.pdf Rhythmic Pattern Modeling for Beat- and Downbeat Tracking in Musical Audio], In Proceedings of 14th International Society for Music Information Retrieval Conference (ISMIR), Curitiba, Brazil, 2013.&lt;br /&gt;
&lt;br /&gt;
M. Mauch, C. Cannam, M. E. P. Davies, S. Dixon, C. Harte, S. Kolozali and D. Tidhar, [http://ismir2009.ismir.net/proceedings/LBD-18.pdf OMRAS2 Metadata Project 2009], Late-breaking session at the 10th International Conference on Music Information Retrieval, 2009.&lt;br /&gt;
&lt;br /&gt;
A. Srinivasamurthy, A. Holzapfel, and Xavier Serra, [http://www.tandfonline.com/doi/full/10.1080/09298215.2013.879902 In Search of Automatic Rhythm Analysis Methods for Turkish and Indian Art Music], Journal of New Music Research, vol. 43, no. 1, pp. 94-114, 2014.&lt;/div&gt;</summary>
		<author><name>Matthew Davies</name></author>
		
	</entry>
	<entry>
		<id>https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10068</id>
		<title>2014:Audio Downbeat Estimation</title>
		<link rel="alternate" type="text/html" href="https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10068"/>
		<updated>2014-05-26T15:51:46Z</updated>

		<summary type="html">&lt;p&gt;Matthew Davies: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Description ==&lt;br /&gt;
&lt;br /&gt;
'''This task is new for 2014!'''&lt;br /&gt;
&lt;br /&gt;
This text has been adapted from the Audio Beat Tracking Wiki page.  Please add your comments and discussion at the bottom of this page.&lt;br /&gt;
&lt;br /&gt;
The aim of the automatic downbeat estimation task is to identify the locations of downbeats in a collection of sound files. While this is similar to the Audio Beat Tracking task, here the aim is to find the first beat of each bar (measure) rather than all beat times. Algorithms are '''not''' required to estimate beat times or time-signature in addition to downbeats.&lt;br /&gt;
&lt;br /&gt;
Submitted algorithms will be evaluated in terms of their accuracy in finding downbeat locations (only) as annotated by musical experts across several diverse datasets.&lt;br /&gt;
&lt;br /&gt;
== Data ==&lt;br /&gt;
&lt;br /&gt;
=== Collections ===&lt;br /&gt;
'''Ballroom'''&lt;br /&gt;
The ballroom dataset contains eight different dance styles (Cha Cha, Jive, Quickstep, Rumba, Samba, Tango, Viennese Waltz and Waltz). It consists of '''697''' excerpts of 30s in duration.&lt;br /&gt;
The dataset contains two different meters 3/4 and 4/4 - but all pieces have constant meter. For further information see Dixon et al (2004) and Krebs et al (2013).&lt;br /&gt;
Note, we are using the ground truth annotations from Krebs et al. (2013) available at https://github.com/CPJKU/BallroomAnnotations&lt;br /&gt;
&lt;br /&gt;
'''Isophonics (Beatles only)'''&lt;br /&gt;
The Beatles dataset from the Centre for Digital Music at Queen Mary, University of London (http://www.isophonics.net/), as also used for Audio Chord Estimation in MIREX for many years. Available from http://www.isophonics.net/. &lt;br /&gt;
This dataset contains '''179''' complete songs (all except Revolution 9), the majority of which are in 4/4.&lt;br /&gt;
For further information see Mauch et al (2009).&lt;br /&gt;
&lt;br /&gt;
'''Turkish Data'''&lt;br /&gt;
The Turkish corpus is an extended version of the annotated data used in Srinivasamurthy et al. (2014). It includes '''82''' excerpts of one&lt;br /&gt;
minute length each, and each piece belongs to one of three&lt;br /&gt;
rhythm classes that are referred to as usul in Turkish Art&lt;br /&gt;
music. 32 pieces are in the 9/8-usul Aksak, 20 pieces&lt;br /&gt;
in the 10/8-usul Curcuna, and 30 samples in the 8/8-usul&lt;br /&gt;
Düyek.&lt;br /&gt;
&lt;br /&gt;
'''Cretan Data'''&lt;br /&gt;
The corpus of Cretan music consists of '''42''' full length pieces of Cretan leaping dances. While there are several dances that differ in terms of their steps, the differences in&lt;br /&gt;
the sound are most noticeable in the melodic content, and all pieces can be considered to belong to one rhythmic style. All these dances are usually notated using a 2/4 time signature,&lt;br /&gt;
and the accompanying rhythmical patterns are usually played on a Cretan lute. While a variety of rhythmic patterns exist, they do not relate to a specific dance and can be&lt;br /&gt;
assumed to occur in all of the 42 songs in this corpus.&lt;br /&gt;
&lt;br /&gt;
'''Carnatic Data'''&lt;br /&gt;
The Carnatic music dataset is a subset of the CompMusic [http://compmusic.upf.edu/carnatic-rhythm-dataset Carnatic Music Rhythm Dataset]. It includes '''118''' two minute long excerpts spanning four most commonly used tālas (the rhythmic framework of Carnatic music, consisting of time cycles) of Carnatic music. There are 30 examples in each of ādi tāla (8 beats/cycle), rūpaka tāla (3 beats/cycle) and miśra chāpu tāla (7 beats/cycle), and 28 examples in khaṇḍa chāpu tāla (5 beats/cycle). The beats of the tāla in miśra chāpu and khaṇḍa chāpu are non-uniform, but for consistency with other datasets, a uniform beat pulse was obtained by interpolating the non-uniformly spaced beat locations. The recordings consist of both vocal and instrumental music recordings representative of the present day performance practice. All recordings contain percussion accompaniment, mainly the Mridangam. &lt;br /&gt;
&lt;br /&gt;
'''HJDB''' (to be confirmed)&lt;br /&gt;
The HJDB dataset contains '''236''' excerpts of Hardcore, Jungle and Drum and Bass music between 30s and 2 minutes in length. All excerpts are in 4/4 and have a constant tempo. &lt;br /&gt;
For further information see Hockman et al (2012).&lt;br /&gt;
&lt;br /&gt;
In total this makes '''1354''' excerpts (of which 259 are full length songs).&lt;br /&gt;
&lt;br /&gt;
=== Audio Formats ===&lt;br /&gt;
&lt;br /&gt;
The data are monophonic sound files&lt;br /&gt;
&lt;br /&gt;
* CD-quality (PCM, 16-bit, 44100 Hz) for all except Ballroom (originally lower quality, but resampled to 44100 Hz)&lt;br /&gt;
* single channel (mono)&lt;br /&gt;
&lt;br /&gt;
== Submission Format ==&lt;br /&gt;
Submissions to this task will have to conform to a specified format detailed below. Submissions should be packaged and contain at least two files: The algorithm itself and a README containing contact information and detailing, in full, the use of the algorithm.&lt;br /&gt;
&lt;br /&gt;
=== Input Data ===&lt;br /&gt;
Participating algorithms will have to read audio in the following format:&lt;br /&gt;
&lt;br /&gt;
* Sample rate: 44.1 KHz&lt;br /&gt;
* Sample size: 16 bit&lt;br /&gt;
* Number of channels: 1 (mono)&lt;br /&gt;
* Encoding: WAV &lt;br /&gt;
&lt;br /&gt;
=== Output Data ===&lt;br /&gt;
&lt;br /&gt;
The downbeat estimation algorithms will return downbeat times in an ASCII text file for each input .wav audio file. The specification of this output file is immediately below.&lt;br /&gt;
&lt;br /&gt;
=== Output File Format (Audio Downbeat Estimation) ===&lt;br /&gt;
&lt;br /&gt;
The downbeat output file format is an ASCII text format. Each downbeat time is specified, in seconds, on its own line. Specifically, &lt;br /&gt;
&lt;br /&gt;
 &amp;lt;downbeat time (in seconds)&amp;gt;\n&lt;br /&gt;
&lt;br /&gt;
where \n denotes the end of line. The &amp;lt; and &amp;gt; characters are not included. An example output file would look something like:&lt;br /&gt;
&lt;br /&gt;
 0.243&lt;br /&gt;
 1.486&lt;br /&gt;
 2.729&lt;br /&gt;
&lt;br /&gt;
=== Algorithm Calling Format ===&lt;br /&gt;
&lt;br /&gt;
The submitted algorithm must take as arguments a SINGLE .wav file to perform the downbeat estimation as well as the full output path and filename of the output file. The ability to specify the output path and file name is essential. Denoting the input .wav file path and name as %input and the output file path and name as %output, a program called foobar could be called from the command-line as follows:&lt;br /&gt;
&lt;br /&gt;
 foobar %input %output&lt;br /&gt;
 foobar -i %input -o %output&lt;br /&gt;
&lt;br /&gt;
Moreover, if your submission takes additional parameters, such as a detection threshold, foobar could be called like:&lt;br /&gt;
&lt;br /&gt;
 foobar .1 %input %output&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output  &lt;br /&gt;
&lt;br /&gt;
If your submission is in MATLAB, it should be submitted as a function. Once again, the function must contain String inputs for the full path and names of the input and output files. Parameters could also be specified as input arguments of the function. For example: &lt;br /&gt;
&lt;br /&gt;
 foobar('%input','%output')&lt;br /&gt;
 foobar(.1,'%input','%output')&lt;br /&gt;
&lt;br /&gt;
=== README File ===&lt;br /&gt;
&lt;br /&gt;
A README file accompanying each submission should contain explicit instructions on how to to run the program (as well as contact information, etc.). In particular, each command line to run should be specified, using %input for the input sound file and %output for the resulting text file.&lt;br /&gt;
&lt;br /&gt;
For instance, to test the program foobar with different values for parameters param1, the README file would look like:&lt;br /&gt;
&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output&lt;br /&gt;
 foobar -param1 .15 -i %input -o %output&lt;br /&gt;
 foobar -param1 .2 -i %input -o %output&lt;br /&gt;
 foobar -param1 .25 -i %input -o %output&lt;br /&gt;
 foobar -param1 .3 -i %input -o %output&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
For a submission using MATLAB, the README file could look like:&lt;br /&gt;
&lt;br /&gt;
 matlab -r &amp;quot;foobar(.1,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.15,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.2,'%input','%output');quit;&amp;quot; &lt;br /&gt;
 matlab -r &amp;quot;foobar(.25,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.3,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
The different command lines to evaluate the performance of each parameter set over the whole database will be generated automatically from each line in the README file containing both '%input' and '%output' strings.&lt;br /&gt;
&lt;br /&gt;
== Evaluation Procedures ==&lt;br /&gt;
&lt;br /&gt;
For the evalution procedure we will use&lt;br /&gt;
*'''F-measure''' - the standard calculation as used in onset and beat tracking evaluation with a +/-70ms window, see Dixon (2007).&lt;br /&gt;
&lt;br /&gt;
Given the high diversity of musical styles included in the task, results will be reported per each invidiual dataset. &lt;br /&gt;
&lt;br /&gt;
== Time and hardware limits ==&lt;br /&gt;
Due to the potentially high number of participants in this and other audio tasks, hard limits on the runtime of submissions will be imposed.&lt;br /&gt;
&lt;br /&gt;
A hard limit of 24 hours will be imposed on analysis times. Submissions exceeding this limit may not receive a result.&lt;br /&gt;
&lt;br /&gt;
== Potential Participants ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
== Discussion ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
= Bibliography =&lt;br /&gt;
&lt;br /&gt;
S. Dixon, F. Gouyon and G. Widmer, [http://ismir2004.ismir.net/proceedings/p093-page-509-paper165.pdf Towards Characterisation of Music via Rhythmic Patterns], In Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR 2004), pp 509-516.&lt;br /&gt;
&lt;br /&gt;
S. Dixon, [http://www.eecs.qmul.ac.uk/~simond/pub/2007/jnmr07.pdf Evaluation of audio beat tracking system BeatRoot], Journal of New Music Research, vol. 36, no. 1, pp. 39-51, 2007.&lt;br /&gt;
&lt;br /&gt;
J. A. Hockman, M. E. P. Davies, I. Fujinaga.[http://ismir2012.ismir.net/event/papers/169-ismir-2012.pdf ONE IN THE JUNGLE: Downbeat Detection in Hardcore, Jungle, and Drum and Bass], In Proceedings of 13th International Society for Music Information Retrieval Conference (ISMIR), Porto, Portugal pp. 169-174, 2012.&lt;br /&gt;
&lt;br /&gt;
F. Krebs, S. Boeck, and G. Widmer, [http://www.cp.jku.at/research/papers/Krebs_etal_ISMIR_2013.pdf Rhythmic Pattern Modeling for Beat- and Downbeat Tracking in Musical Audio], In Proceedings of 14th International Society for Music Information Retrieval Conference (ISMIR), Curitiba, Brazil, 2013.&lt;br /&gt;
&lt;br /&gt;
M. Mauch, C. Cannam, M. E. P. Davies, S. Dixon, C. Harte, S. Kolozali and D. Tidhar, [http://ismir2009.ismir.net/proceedings/LBD-18.pdf OMRAS2 Metadata Project 2009], Late-breaking session at the 10th International Conference on Music Information Retrieval, 2009.&lt;br /&gt;
&lt;br /&gt;
A. Srinivasamurthy, A. Holzapfel, and Xavier Serra, [http://www.tandfonline.com/doi/full/10.1080/09298215.2013.879902 In Search of Automatic Rhythm Analysis Methods for Turkish and Indian Art Music], Journal of New Music Research, vol. 43, no. 1, pp. 94-114, 2014.&lt;/div&gt;</summary>
		<author><name>Matthew Davies</name></author>
		
	</entry>
	<entry>
		<id>https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10067</id>
		<title>2014:Audio Downbeat Estimation</title>
		<link rel="alternate" type="text/html" href="https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10067"/>
		<updated>2014-05-26T15:47:33Z</updated>

		<summary type="html">&lt;p&gt;Matthew Davies: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Description ==&lt;br /&gt;
&lt;br /&gt;
'''This task is new for 2014!'''&lt;br /&gt;
&lt;br /&gt;
This text has been adapted from the Audio Beat Tracking Wiki page.  Please add your comments and discussion at the bottom of this page.&lt;br /&gt;
&lt;br /&gt;
The aim of the automatic downbeat estimation task is to identify the locations of downbeats in a collection of sound files. While this is similar to the Audio Beat Tracking task, here the aim is to find the first beat of each bar (measure) rather than all beat times. Algorithms are '''not''' required to estimate beat times or time-signature in addition to downbeats.&lt;br /&gt;
&lt;br /&gt;
Submitted algorithms will be evaluated in terms of their accuracy in finding downbeat locations (only) as annotated by musical experts across several diverse datasets.&lt;br /&gt;
&lt;br /&gt;
== Data ==&lt;br /&gt;
&lt;br /&gt;
=== Collections ===&lt;br /&gt;
'''Ballroom'''&lt;br /&gt;
The ballroom dataset contains eight different dance styles (Cha Cha, Jive, Quickstep, Rumba, Samba, Tango, Viennese Waltz and Waltz). It consists of '''697''' excerpts of 30s in duration.&lt;br /&gt;
The dataset contains two different meters 3/4 and 4/4 - but all pieces have constant meter. For further information see Dixon et al (2004) and Krebs et al (2013).&lt;br /&gt;
Note, we are using the ground truth annotations from Krebs et al. (2013) available at https://github.com/CPJKU/BallroomAnnotations&lt;br /&gt;
&lt;br /&gt;
'''Isophonics (Beatles only)'''&lt;br /&gt;
The Beatles dataset from the Centre for Digital Music at Queen Mary, University of London (http://www.isophonics.net/), as also used for Audio Chord Estimation in MIREX for many years. Available from http://www.isophonics.net/. &lt;br /&gt;
This dataset contains '''179''' complete songs (all except Revolution 9), the majority of which are in 4/4.&lt;br /&gt;
For further information see Mauch et al (2009).&lt;br /&gt;
&lt;br /&gt;
'''Turkish Data'''&lt;br /&gt;
The Turkish corpus is an extended version of the annotated data used in Srinivasamurthy et al. (2014). It includes '''82''' excerpts of one&lt;br /&gt;
minute length each, and each piece belongs to one of three&lt;br /&gt;
rhythm classes that are referred to as usul in Turkish Art&lt;br /&gt;
music. 32 pieces are in the 9/8-usul Aksak, 20 pieces&lt;br /&gt;
in the 10/8-usul Curcuna, and 30 samples in the 8/8-usul&lt;br /&gt;
Düyek.&lt;br /&gt;
&lt;br /&gt;
'''Cretan Data'''&lt;br /&gt;
The corpus of Cretan music consists of '''42''' full length pieces of Cretan leaping dances. While there are several dances that differ in terms of their steps, the differences in&lt;br /&gt;
the sound are most noticeable in the melodic content, and all pieces can be considered to belong to one rhythmic style. All these dances are usually notated using a 2/4 time signature,&lt;br /&gt;
and the accompanying rhythmical patterns are usually played on a Cretan lute. While a variety of rhythmic patterns exist, they do not relate to a specific dance and can be&lt;br /&gt;
assumed to occur in all of the 42 songs in this corpus.&lt;br /&gt;
&lt;br /&gt;
'''Carnatic Data'''&lt;br /&gt;
The Carnatic music dataset is a subset of the CompMusic [http://compmusic.upf.edu/carnatic-rhythm-dataset Carnatic Music Rhythm Dataset]. It includes '''118''' two minute long excerpts spanning four most commonly used tālas (the rhythmic framework of Carnatic music, consisting of time cycles) of Carnatic music. There are 30 examples in each of ādi tāla (8 beats/cycle), rūpaka tāla (3 beats/cycle) and miśra chāpu tāla (7 beats/cycle), and 28 examples in khaṇḍa chāpu tāla (5 beats/cycle). The beats of the tāla in miśra chāpu and khaṇḍa chāpu are non-uniform, but for consistency with other datasets, a uniform beat pulse was obtained by interpolating the non-uniformly spaced beat locations. The recordings consist of both vocal and instrumental music recordings representative of the present day performance practice. All recordings contain percussion accompaniment, mainly the Mridangam. &lt;br /&gt;
&lt;br /&gt;
'''HJDB''' (to be confirmed)&lt;br /&gt;
The HJDB dataset contains '''236''' excerpts of Hardcore, Jungle and Drum and Bass music between 30s and 2 minutes in length. All excerpts are in 4/4 and have a constant tempo. &lt;br /&gt;
For further information see Hockman et al (2012).&lt;br /&gt;
&lt;br /&gt;
In total this makes '''1354''' excerpts (of which 259 are full length songs).&lt;br /&gt;
&lt;br /&gt;
=== Audio Formats ===&lt;br /&gt;
&lt;br /&gt;
The data are monophonic sound files, with the associated onset times and data about the annotation robustness.&lt;br /&gt;
&lt;br /&gt;
* CD-quality (PCM, 16-bit, 44100 Hz) for all except Ballroom (originally lower quality, but resampled to 44100 Hz)&lt;br /&gt;
* single channel (mono)&lt;br /&gt;
&lt;br /&gt;
== Submission Format ==&lt;br /&gt;
Submissions to this task will have to conform to a specified format detailed below. Submissions should be packaged and contain at least two files: The algorithm itself and a README containing contact information and detailing, in full, the use of the algorithm.&lt;br /&gt;
&lt;br /&gt;
=== Input Data ===&lt;br /&gt;
Participating algorithms will have to read audio in the following format:&lt;br /&gt;
&lt;br /&gt;
* Sample rate: 44.1 KHz&lt;br /&gt;
* Sample size: 16 bit&lt;br /&gt;
* Number of channels: 1 (mono)&lt;br /&gt;
* Encoding: WAV &lt;br /&gt;
&lt;br /&gt;
=== Output Data ===&lt;br /&gt;
&lt;br /&gt;
The downbeat estimation algorithms will return downbeat times in an ASCII text file for each input .wav audio file. The specification of this output file is immediately below.&lt;br /&gt;
&lt;br /&gt;
=== Output File Format (Audio Downbeat Estimation) ===&lt;br /&gt;
&lt;br /&gt;
The downbeat output file format is an ASCII text format. Each downbeat time is specified, in seconds, on its own line. Specifically, &lt;br /&gt;
&lt;br /&gt;
 &amp;lt;downbeat time (in seconds)&amp;gt;\n&lt;br /&gt;
&lt;br /&gt;
where \n denotes the end of line. The &amp;lt; and &amp;gt; characters are not included. An example output file would look something like:&lt;br /&gt;
&lt;br /&gt;
 0.243&lt;br /&gt;
 1.486&lt;br /&gt;
 2.729&lt;br /&gt;
&lt;br /&gt;
=== Algorithm Calling Format ===&lt;br /&gt;
&lt;br /&gt;
The submitted algorithm must take as arguments a SINGLE .wav file to perform the downbeat estimation as well as the full output path and filename of the output file. The ability to specify the output path and file name is essential. Denoting the input .wav file path and name as %input and the output file path and name as %output, a program called foobar could be called from the command-line as follows:&lt;br /&gt;
&lt;br /&gt;
 foobar %input %output&lt;br /&gt;
 foobar -i %input -o %output&lt;br /&gt;
&lt;br /&gt;
Moreover, if your submission takes additional parameters, such as a detection threshold, foobar could be called like:&lt;br /&gt;
&lt;br /&gt;
 foobar .1 %input %output&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output  &lt;br /&gt;
&lt;br /&gt;
If your submission is in MATLAB, it should be submitted as a function. Once again, the function must contain String inputs for the full path and names of the input and output files. Parameters could also be specified as input arguments of the function. For example: &lt;br /&gt;
&lt;br /&gt;
 foobar('%input','%output')&lt;br /&gt;
 foobar(.1,'%input','%output')&lt;br /&gt;
&lt;br /&gt;
=== README File ===&lt;br /&gt;
&lt;br /&gt;
A README file accompanying each submission should contain explicit instructions on how to to run the program (as well as contact information, etc.). In particular, each command line to run should be specified, using %input for the input sound file and %output for the resulting text file.&lt;br /&gt;
&lt;br /&gt;
For instance, to test the program foobar with different values for parameters param1, the README file would look like:&lt;br /&gt;
&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output&lt;br /&gt;
 foobar -param1 .15 -i %input -o %output&lt;br /&gt;
 foobar -param1 .2 -i %input -o %output&lt;br /&gt;
 foobar -param1 .25 -i %input -o %output&lt;br /&gt;
 foobar -param1 .3 -i %input -o %output&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
For a submission using MATLAB, the README file could look like:&lt;br /&gt;
&lt;br /&gt;
 matlab -r &amp;quot;foobar(.1,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.15,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.2,'%input','%output');quit;&amp;quot; &lt;br /&gt;
 matlab -r &amp;quot;foobar(.25,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.3,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
The different command lines to evaluate the performance of each parameter set over the whole database will be generated automatically from each line in the README file containing both '%input' and '%output' strings.&lt;br /&gt;
&lt;br /&gt;
== Evaluation Procedures ==&lt;br /&gt;
&lt;br /&gt;
For the evalution procedure we will use&lt;br /&gt;
*'''F-measure''' - the standard calculation as used in onset and beat tracking evaluation with a +/-70ms window, see Dixon (2007).&lt;br /&gt;
&lt;br /&gt;
Given the high diversity of musical styles included in the task, results will be reported per each invidiual dataset. &lt;br /&gt;
&lt;br /&gt;
== Time and hardware limits ==&lt;br /&gt;
Due to the potentially high number of participants in this and other audio tasks, hard limits on the runtime of submissions will be imposed.&lt;br /&gt;
&lt;br /&gt;
A hard limit of 24 hours will be imposed on analysis times. Submissions exceeding this limit may not receive a result.&lt;br /&gt;
&lt;br /&gt;
== Potential Participants ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
== Discussion ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
= Bibliography =&lt;br /&gt;
&lt;br /&gt;
S. Dixon, F. Gouyon and G. Widmer, [http://ismir2004.ismir.net/proceedings/p093-page-509-paper165.pdf Towards Characterisation of Music via Rhythmic Patterns], In Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR 2004), pp 509-516.&lt;br /&gt;
&lt;br /&gt;
S. Dixon, [http://www.eecs.qmul.ac.uk/~simond/pub/2007/jnmr07.pdf Evaluation of audio beat tracking system BeatRoot], Journal of New Music Research, vol. 36, no. 1, pp. 39-51, 2007.&lt;br /&gt;
&lt;br /&gt;
J. A. Hockman, M. E. P. Davies, I. Fujinaga.[http://ismir2012.ismir.net/event/papers/169-ismir-2012.pdf ONE IN THE JUNGLE: Downbeat Detection in Hardcore, Jungle, and Drum and Bass], In Proceedings of 13th International Society for Music Information Retrieval Conference (ISMIR), Porto, Portugal pp. 169-174, 2012.&lt;br /&gt;
&lt;br /&gt;
F. Krebs, S. Boeck, and G. Widmer, [http://www.cp.jku.at/research/papers/Krebs_etal_ISMIR_2013.pdf Rhythmic Pattern Modeling for Beat- and Downbeat Tracking in Musical Audio], In Proceedings of 14th International Society for Music Information Retrieval Conference (ISMIR), Curitiba, Brazil, 2013.&lt;br /&gt;
&lt;br /&gt;
M. Mauch, C. Cannam, M. E. P. Davies, S. Dixon, C. Harte, S. Kolozali and D. Tidhar, [http://ismir2009.ismir.net/proceedings/LBD-18.pdf OMRAS2 Metadata Project 2009], Late-breaking session at the 10th International Conference on Music Information Retrieval, 2009.&lt;br /&gt;
&lt;br /&gt;
A. Srinivasamurthy, A. Holzapfel, and Xavier Serra, [http://www.tandfonline.com/doi/full/10.1080/09298215.2013.879902 In Search of Automatic Rhythm Analysis Methods for Turkish and Indian Art Music], Journal of New Music Research, vol. 43, no. 1, pp. 94-114, 2014.&lt;/div&gt;</summary>
		<author><name>Matthew Davies</name></author>
		
	</entry>
	<entry>
		<id>https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10066</id>
		<title>2014:Audio Downbeat Estimation</title>
		<link rel="alternate" type="text/html" href="https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10066"/>
		<updated>2014-05-26T15:47:13Z</updated>

		<summary type="html">&lt;p&gt;Matthew Davies: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Description ==&lt;br /&gt;
&lt;br /&gt;
'''This task is new for 2014!'''&lt;br /&gt;
&lt;br /&gt;
This text has been adapted from the Audio Beat Tracking Wiki page.  Please add your comments and discussion at the bottom of this page.&lt;br /&gt;
&lt;br /&gt;
The aim of the automatic downbeat estimation task is to identify the locations of downbeats in a collection of sound files. While this is similar to the Audio Beat Tracking task, here the aim is to find the first beat of each bar (measure) rather than all beat times. Algorithms are '''not''' required to estimate beat times or time-signature in addition to downbeats.&lt;br /&gt;
&lt;br /&gt;
Submitted algorithms will be evaluated in terms of their accuracy in finding downbeat locations (only) as annotated by musical experts across several diverse datasets.&lt;br /&gt;
&lt;br /&gt;
== Data ==&lt;br /&gt;
&lt;br /&gt;
=== Collections ===&lt;br /&gt;
'''Ballroom'''&lt;br /&gt;
The ballroom dataset contains eight different dance styles (Cha Cha, Jive, Quickstep, Rumba, Samba, Tango, Viennese Waltz and Waltz). It consists of '''697''' excerpts of 30s in duration.&lt;br /&gt;
The dataset contains two different meters 3/4 and 4/4 - but all pieces have constant meter. For further information see Dixon et al (2004) and Krebs et al (2013).&lt;br /&gt;
Note, we are using the ground truth annotations from Krebs et al. (2013) available at https://github.com/CPJKU/BallroomAnnotations&lt;br /&gt;
&lt;br /&gt;
'''Isophonics (Beatles only)'''&lt;br /&gt;
The Beatles dataset from the Centre for Digital Music at Queen Mary, University of London (http://www.isophonics.net/), as also used for Audio Chord Estimation in MIREX for many years. Available from http://www.isophonics.net/. &lt;br /&gt;
This dataset contains '''179''' complete songs (all except Revolution 9), the majority of which are in 4/4.&lt;br /&gt;
For further information see Mauch et al (2009).&lt;br /&gt;
&lt;br /&gt;
'''Turkish Data'''&lt;br /&gt;
The Turkish corpus is an extended version of the annotated data used in Srinivasamurthy et al. (2014). It includes '''82''' excerpts of one&lt;br /&gt;
minute length each, and each piece belongs to one of three&lt;br /&gt;
rhythm classes that are referred to as usul in Turkish Art&lt;br /&gt;
music. 32 pieces are in the 9/8-usul Aksak, 20 pieces&lt;br /&gt;
in the 10/8-usul Curcuna, and 30 samples in the 8/8-usul&lt;br /&gt;
Düyek.&lt;br /&gt;
&lt;br /&gt;
'''Cretan Data'''&lt;br /&gt;
The corpus of Cretan music consists of '''42''' full length pieces of Cretan leaping dances. While there are several dances that differ in terms of their steps, the differences in&lt;br /&gt;
the sound are most noticeable in the melodic content, and all pieces can be considered to belong to one rhythmic style. All these dances are usually notated using a 2/4 time signature,&lt;br /&gt;
and the accompanying rhythmical patterns are usually played on a Cretan lute. While a variety of rhythmic patterns exist, they do not relate to a specific dance and can be&lt;br /&gt;
assumed to occur in all of the 42 songs in this corpus.&lt;br /&gt;
&lt;br /&gt;
'''Carnatic Data'''&lt;br /&gt;
The Carnatic music dataset is a subset of the CompMusic [http://compmusic.upf.edu/carnatic-rhythm-dataset Carnatic Music Rhythm Dataset]. It includes '''118''' two minute long excerpts spanning four most commonly used tālas (the rhythmic framework of Carnatic music, consisting of time cycles) of Carnatic music. There are 30 examples in each of ādi tāla (8 beats/cycle), rūpaka tāla (3 beats/cycle) and miśra chāpu tāla (7 beats/cycle), and 28 examples in khaṇḍa chāpu tāla (5 beats/cycle). The beats of the tāla in miśra chāpu and khaṇḍa chāpu are non-uniform, but for consistency with other datasets, a uniform beat pulse was obtained by interpolating the non-uniformly spaced beat locations. The recordings consist of both vocal and instrumental music recordings representative of the present day performance practice. All recordings contain percussion accompaniment, mainly the Mridangam. &lt;br /&gt;
&lt;br /&gt;
'''HJDB''' (to be confirmed)&lt;br /&gt;
The HJDB dataset contains '''236''' excerpts of Hardcore, Jungle and Drum and Bass music between 30s and 2 minutes in length. All excerpts are in 4/4 and have a constant tempo. &lt;br /&gt;
For further information see Hockman et al (2012).&lt;br /&gt;
&lt;br /&gt;
In total this makes 1354 excerpts (of which 259 are full length songs).&lt;br /&gt;
&lt;br /&gt;
=== Audio Formats ===&lt;br /&gt;
&lt;br /&gt;
The data are monophonic sound files, with the associated onset times and data about the annotation robustness.&lt;br /&gt;
&lt;br /&gt;
* CD-quality (PCM, 16-bit, 44100 Hz) for all except Ballroom (originally lower quality, but resampled to 44100 Hz)&lt;br /&gt;
* single channel (mono)&lt;br /&gt;
&lt;br /&gt;
== Submission Format ==&lt;br /&gt;
Submissions to this task will have to conform to a specified format detailed below. Submissions should be packaged and contain at least two files: The algorithm itself and a README containing contact information and detailing, in full, the use of the algorithm.&lt;br /&gt;
&lt;br /&gt;
=== Input Data ===&lt;br /&gt;
Participating algorithms will have to read audio in the following format:&lt;br /&gt;
&lt;br /&gt;
* Sample rate: 44.1 KHz&lt;br /&gt;
* Sample size: 16 bit&lt;br /&gt;
* Number of channels: 1 (mono)&lt;br /&gt;
* Encoding: WAV &lt;br /&gt;
&lt;br /&gt;
=== Output Data ===&lt;br /&gt;
&lt;br /&gt;
The downbeat estimation algorithms will return downbeat times in an ASCII text file for each input .wav audio file. The specification of this output file is immediately below.&lt;br /&gt;
&lt;br /&gt;
=== Output File Format (Audio Downbeat Estimation) ===&lt;br /&gt;
&lt;br /&gt;
The downbeat output file format is an ASCII text format. Each downbeat time is specified, in seconds, on its own line. Specifically, &lt;br /&gt;
&lt;br /&gt;
 &amp;lt;downbeat time (in seconds)&amp;gt;\n&lt;br /&gt;
&lt;br /&gt;
where \n denotes the end of line. The &amp;lt; and &amp;gt; characters are not included. An example output file would look something like:&lt;br /&gt;
&lt;br /&gt;
 0.243&lt;br /&gt;
 1.486&lt;br /&gt;
 2.729&lt;br /&gt;
&lt;br /&gt;
=== Algorithm Calling Format ===&lt;br /&gt;
&lt;br /&gt;
The submitted algorithm must take as arguments a SINGLE .wav file to perform the downbeat estimation as well as the full output path and filename of the output file. The ability to specify the output path and file name is essential. Denoting the input .wav file path and name as %input and the output file path and name as %output, a program called foobar could be called from the command-line as follows:&lt;br /&gt;
&lt;br /&gt;
 foobar %input %output&lt;br /&gt;
 foobar -i %input -o %output&lt;br /&gt;
&lt;br /&gt;
Moreover, if your submission takes additional parameters, such as a detection threshold, foobar could be called like:&lt;br /&gt;
&lt;br /&gt;
 foobar .1 %input %output&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output  &lt;br /&gt;
&lt;br /&gt;
If your submission is in MATLAB, it should be submitted as a function. Once again, the function must contain String inputs for the full path and names of the input and output files. Parameters could also be specified as input arguments of the function. For example: &lt;br /&gt;
&lt;br /&gt;
 foobar('%input','%output')&lt;br /&gt;
 foobar(.1,'%input','%output')&lt;br /&gt;
&lt;br /&gt;
=== README File ===&lt;br /&gt;
&lt;br /&gt;
A README file accompanying each submission should contain explicit instructions on how to to run the program (as well as contact information, etc.). In particular, each command line to run should be specified, using %input for the input sound file and %output for the resulting text file.&lt;br /&gt;
&lt;br /&gt;
For instance, to test the program foobar with different values for parameters param1, the README file would look like:&lt;br /&gt;
&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output&lt;br /&gt;
 foobar -param1 .15 -i %input -o %output&lt;br /&gt;
 foobar -param1 .2 -i %input -o %output&lt;br /&gt;
 foobar -param1 .25 -i %input -o %output&lt;br /&gt;
 foobar -param1 .3 -i %input -o %output&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
For a submission using MATLAB, the README file could look like:&lt;br /&gt;
&lt;br /&gt;
 matlab -r &amp;quot;foobar(.1,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.15,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.2,'%input','%output');quit;&amp;quot; &lt;br /&gt;
 matlab -r &amp;quot;foobar(.25,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.3,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
The different command lines to evaluate the performance of each parameter set over the whole database will be generated automatically from each line in the README file containing both '%input' and '%output' strings.&lt;br /&gt;
&lt;br /&gt;
== Evaluation Procedures ==&lt;br /&gt;
&lt;br /&gt;
For the evalution procedure we will use&lt;br /&gt;
*'''F-measure''' - the standard calculation as used in onset and beat tracking evaluation with a +/-70ms window, see Dixon (2007).&lt;br /&gt;
&lt;br /&gt;
Given the high diversity of musical styles included in the task, results will be reported per each invidiual dataset. &lt;br /&gt;
&lt;br /&gt;
== Time and hardware limits ==&lt;br /&gt;
Due to the potentially high number of participants in this and other audio tasks, hard limits on the runtime of submissions will be imposed.&lt;br /&gt;
&lt;br /&gt;
A hard limit of 24 hours will be imposed on analysis times. Submissions exceeding this limit may not receive a result.&lt;br /&gt;
&lt;br /&gt;
== Potential Participants ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
== Discussion ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
= Bibliography =&lt;br /&gt;
&lt;br /&gt;
S. Dixon, F. Gouyon and G. Widmer, [http://ismir2004.ismir.net/proceedings/p093-page-509-paper165.pdf Towards Characterisation of Music via Rhythmic Patterns], In Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR 2004), pp 509-516.&lt;br /&gt;
&lt;br /&gt;
S. Dixon, [http://www.eecs.qmul.ac.uk/~simond/pub/2007/jnmr07.pdf Evaluation of audio beat tracking system BeatRoot], Journal of New Music Research, vol. 36, no. 1, pp. 39-51, 2007.&lt;br /&gt;
&lt;br /&gt;
J. A. Hockman, M. E. P. Davies, I. Fujinaga.[http://ismir2012.ismir.net/event/papers/169-ismir-2012.pdf ONE IN THE JUNGLE: Downbeat Detection in Hardcore, Jungle, and Drum and Bass], In Proceedings of 13th International Society for Music Information Retrieval Conference (ISMIR), Porto, Portugal pp. 169-174, 2012.&lt;br /&gt;
&lt;br /&gt;
F. Krebs, S. Boeck, and G. Widmer, [http://www.cp.jku.at/research/papers/Krebs_etal_ISMIR_2013.pdf Rhythmic Pattern Modeling for Beat- and Downbeat Tracking in Musical Audio], In Proceedings of 14th International Society for Music Information Retrieval Conference (ISMIR), Curitiba, Brazil, 2013.&lt;br /&gt;
&lt;br /&gt;
M. Mauch, C. Cannam, M. E. P. Davies, S. Dixon, C. Harte, S. Kolozali and D. Tidhar, [http://ismir2009.ismir.net/proceedings/LBD-18.pdf OMRAS2 Metadata Project 2009], Late-breaking session at the 10th International Conference on Music Information Retrieval, 2009.&lt;br /&gt;
&lt;br /&gt;
A. Srinivasamurthy, A. Holzapfel, and Xavier Serra, [http://www.tandfonline.com/doi/full/10.1080/09298215.2013.879902 In Search of Automatic Rhythm Analysis Methods for Turkish and Indian Art Music], Journal of New Music Research, vol. 43, no. 1, pp. 94-114, 2014.&lt;/div&gt;</summary>
		<author><name>Matthew Davies</name></author>
		
	</entry>
	<entry>
		<id>https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10065</id>
		<title>2014:Audio Downbeat Estimation</title>
		<link rel="alternate" type="text/html" href="https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10065"/>
		<updated>2014-05-26T15:45:27Z</updated>

		<summary type="html">&lt;p&gt;Matthew Davies: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Description ==&lt;br /&gt;
&lt;br /&gt;
'''This task is new for 2014!'''&lt;br /&gt;
&lt;br /&gt;
This text has been adapted from the Audio Beat Tracking Wiki page.  Please add your comments and discussion at the bottom of this page.&lt;br /&gt;
&lt;br /&gt;
The aim of the automatic downbeat estimation task is to identify the locations of downbeats in a collection of sound files. While this is similar to the Audio Beat Tracking task, here the aim is to find the first beat of each bar (measure) rather than all beat times. Algorithms are '''not''' required to estimate beat times or time-signature in addition to downbeats.&lt;br /&gt;
&lt;br /&gt;
Submitted algorithms will be evaluated in terms of their accuracy in finding downbeat locations (only) as annotated by musical experts across several diverse datasets.&lt;br /&gt;
&lt;br /&gt;
== Data ==&lt;br /&gt;
&lt;br /&gt;
=== Collections ===&lt;br /&gt;
'''Ballroom'''&lt;br /&gt;
The ballroom dataset contains eight different dance styles (Cha Cha, Jive, Quickstep, Rumba, Samba, Tango, Viennese Waltz and Waltz). It consists of 697 excerpts of 30s in duration.&lt;br /&gt;
The dataset contains two different meters 3/4 and 4/4 - but all pieces have constant meter. For further information see Dixon et al (2004) and Krebs et al (2013).&lt;br /&gt;
Note, we are using the ground truth annotations from Krebs et al. (2013) available at https://github.com/CPJKU/BallroomAnnotations&lt;br /&gt;
&lt;br /&gt;
'''Isophonics (Beatles only)'''&lt;br /&gt;
The Beatles dataset from the Centre for Digital Music at Queen Mary, University of London (http://www.isophonics.net/), as also used for Audio Chord Estimation in MIREX for many years. Available from http://www.isophonics.net/. &lt;br /&gt;
This dataset contains 179 complete songs (all except Revolution 9), the majority of which are in 4/4.&lt;br /&gt;
For further information see Mauch et al (2009).&lt;br /&gt;
&lt;br /&gt;
'''Turkish Data'''&lt;br /&gt;
The Turkish corpus is an extended version of the annotated data used in Srinivasamurthy et al. (2014). It includes 82 excerpts of one&lt;br /&gt;
minute length each, and each piece belongs to one of three&lt;br /&gt;
rhythm classes that are referred to as usul in Turkish Art&lt;br /&gt;
music. 32 pieces are in the 9/8-usul Aksak, 20 pieces&lt;br /&gt;
in the 10/8-usul Curcuna, and 30 samples in the 8/8-usul&lt;br /&gt;
Düyek.&lt;br /&gt;
&lt;br /&gt;
'''Cretan Data'''&lt;br /&gt;
The corpus of Cretan music consists of 42 full length pieces of Cretan leaping dances. While there are several dances that differ in terms of their steps, the differences in&lt;br /&gt;
the sound are most noticeable in the melodic content, and all pieces can be considered to belong to one rhythmic style. All these dances are usually notated using a 2/4 time signature,&lt;br /&gt;
and the accompanying rhythmical patterns are usually played on a Cretan lute. While a variety of rhythmic patterns exist, they do not relate to a specific dance and can be&lt;br /&gt;
assumed to occur in all of the 42 songs in this corpus.&lt;br /&gt;
&lt;br /&gt;
'''Carnatic Data'''&lt;br /&gt;
The Carnatic music dataset is a subset of the CompMusic [http://compmusic.upf.edu/carnatic-rhythm-dataset Carnatic Music Rhythm Dataset]. It includes 118 two minute long excerpts spanning four most commonly used tālas (the rhythmic framework of Carnatic music, consisting of time cycles) of Carnatic music. There are 30 examples in each of ādi tāla (8 beats/cycle), rūpaka tāla (3 beats/cycle) and miśra chāpu tāla (7 beats/cycle), and 28 examples in khaṇḍa chāpu tāla (5 beats/cycle). The beats of the tāla in miśra chāpu and khaṇḍa chāpu are non-uniform, but for consistency with other datasets, a uniform beat pulse was obtained by interpolating the non-uniformly spaced beat locations. The recordings consist of both vocal and instrumental music recordings representative of the present day performance practice. All recordings contain percussion accompaniment, mainly the Mridangam. &lt;br /&gt;
&lt;br /&gt;
'''HJDB''' (to be confirmed)&lt;br /&gt;
236 excerpts of Hardcore, Jungle and Drum and Bass music between 30s and 2 minutes in length. All excerpts are in 4/4 and have a constant tempo. &lt;br /&gt;
For further information see Hockman et al (2012).&lt;br /&gt;
&lt;br /&gt;
In total this makes 1234 excerpts (of which 259 are full length songs).&lt;br /&gt;
&lt;br /&gt;
=== Audio Formats ===&lt;br /&gt;
&lt;br /&gt;
The data are monophonic sound files, with the associated onset times and data about the annotation robustness.&lt;br /&gt;
&lt;br /&gt;
* CD-quality (PCM, 16-bit, 44100 Hz) for all except Ballroom (originally lower quality, but resampled to 44100 Hz)&lt;br /&gt;
* single channel (mono)&lt;br /&gt;
&lt;br /&gt;
== Submission Format ==&lt;br /&gt;
Submissions to this task will have to conform to a specified format detailed below. Submissions should be packaged and contain at least two files: The algorithm itself and a README containing contact information and detailing, in full, the use of the algorithm.&lt;br /&gt;
&lt;br /&gt;
=== Input Data ===&lt;br /&gt;
Participating algorithms will have to read audio in the following format:&lt;br /&gt;
&lt;br /&gt;
* Sample rate: 44.1 KHz&lt;br /&gt;
* Sample size: 16 bit&lt;br /&gt;
* Number of channels: 1 (mono)&lt;br /&gt;
* Encoding: WAV &lt;br /&gt;
&lt;br /&gt;
=== Output Data ===&lt;br /&gt;
&lt;br /&gt;
The downbeat estimation algorithms will return downbeat times in an ASCII text file for each input .wav audio file. The specification of this output file is immediately below.&lt;br /&gt;
&lt;br /&gt;
=== Output File Format (Audio Downbeat Estimation) ===&lt;br /&gt;
&lt;br /&gt;
The downbeat output file format is an ASCII text format. Each downbeat time is specified, in seconds, on its own line. Specifically, &lt;br /&gt;
&lt;br /&gt;
 &amp;lt;downbeat time (in seconds)&amp;gt;\n&lt;br /&gt;
&lt;br /&gt;
where \n denotes the end of line. The &amp;lt; and &amp;gt; characters are not included. An example output file would look something like:&lt;br /&gt;
&lt;br /&gt;
 0.243&lt;br /&gt;
 1.486&lt;br /&gt;
 2.729&lt;br /&gt;
&lt;br /&gt;
=== Algorithm Calling Format ===&lt;br /&gt;
&lt;br /&gt;
The submitted algorithm must take as arguments a SINGLE .wav file to perform the downbeat estimation as well as the full output path and filename of the output file. The ability to specify the output path and file name is essential. Denoting the input .wav file path and name as %input and the output file path and name as %output, a program called foobar could be called from the command-line as follows:&lt;br /&gt;
&lt;br /&gt;
 foobar %input %output&lt;br /&gt;
 foobar -i %input -o %output&lt;br /&gt;
&lt;br /&gt;
Moreover, if your submission takes additional parameters, such as a detection threshold, foobar could be called like:&lt;br /&gt;
&lt;br /&gt;
 foobar .1 %input %output&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output  &lt;br /&gt;
&lt;br /&gt;
If your submission is in MATLAB, it should be submitted as a function. Once again, the function must contain String inputs for the full path and names of the input and output files. Parameters could also be specified as input arguments of the function. For example: &lt;br /&gt;
&lt;br /&gt;
 foobar('%input','%output')&lt;br /&gt;
 foobar(.1,'%input','%output')&lt;br /&gt;
&lt;br /&gt;
=== README File ===&lt;br /&gt;
&lt;br /&gt;
A README file accompanying each submission should contain explicit instructions on how to to run the program (as well as contact information, etc.). In particular, each command line to run should be specified, using %input for the input sound file and %output for the resulting text file.&lt;br /&gt;
&lt;br /&gt;
For instance, to test the program foobar with different values for parameters param1, the README file would look like:&lt;br /&gt;
&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output&lt;br /&gt;
 foobar -param1 .15 -i %input -o %output&lt;br /&gt;
 foobar -param1 .2 -i %input -o %output&lt;br /&gt;
 foobar -param1 .25 -i %input -o %output&lt;br /&gt;
 foobar -param1 .3 -i %input -o %output&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
For a submission using MATLAB, the README file could look like:&lt;br /&gt;
&lt;br /&gt;
 matlab -r &amp;quot;foobar(.1,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.15,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.2,'%input','%output');quit;&amp;quot; &lt;br /&gt;
 matlab -r &amp;quot;foobar(.25,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.3,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
The different command lines to evaluate the performance of each parameter set over the whole database will be generated automatically from each line in the README file containing both '%input' and '%output' strings.&lt;br /&gt;
&lt;br /&gt;
== Evaluation Procedures ==&lt;br /&gt;
&lt;br /&gt;
For the evalution procedure we will use&lt;br /&gt;
*'''F-measure''' - the standard calculation as used in onset and beat tracking evaluation with a +/-70ms window, see Dixon (2007).&lt;br /&gt;
&lt;br /&gt;
Given the high diversity of musical styles included in the task, results will be reported per each invidiual dataset. &lt;br /&gt;
&lt;br /&gt;
== Time and hardware limits ==&lt;br /&gt;
Due to the potentially high number of participants in this and other audio tasks, hard limits on the runtime of submissions will be imposed.&lt;br /&gt;
&lt;br /&gt;
A hard limit of 24 hours will be imposed on analysis times. Submissions exceeding this limit may not receive a result.&lt;br /&gt;
&lt;br /&gt;
== Potential Participants ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
== Discussion ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
= Bibliography =&lt;br /&gt;
&lt;br /&gt;
S. Dixon, F. Gouyon and G. Widmer, [http://ismir2004.ismir.net/proceedings/p093-page-509-paper165.pdf Towards Characterisation of Music via Rhythmic Patterns], In Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR 2004), pp 509-516.&lt;br /&gt;
&lt;br /&gt;
S. Dixon, [http://www.eecs.qmul.ac.uk/~simond/pub/2007/jnmr07.pdf Evaluation of audio beat tracking system BeatRoot], Journal of New Music Research, vol. 36, no. 1, pp. 39-51, 2007.&lt;br /&gt;
&lt;br /&gt;
J. A. Hockman, M. E. P. Davies, I. Fujinaga.[http://ismir2012.ismir.net/event/papers/169-ismir-2012.pdf ONE IN THE JUNGLE: Downbeat Detection in Hardcore, Jungle, and Drum and Bass], In Proceedings of 13th International Society for Music Information Retrieval Conference (ISMIR), Porto, Portugal pp. 169-174, 2012.&lt;br /&gt;
&lt;br /&gt;
F. Krebs, S. Boeck, and G. Widmer, [http://www.cp.jku.at/research/papers/Krebs_etal_ISMIR_2013.pdf Rhythmic Pattern Modeling for Beat- and Downbeat Tracking in Musical Audio], In Proceedings of 14th International Society for Music Information Retrieval Conference (ISMIR), Curitiba, Brazil, 2013.&lt;br /&gt;
&lt;br /&gt;
M. Mauch, C. Cannam, M. E. P. Davies, S. Dixon, C. Harte, S. Kolozali and D. Tidhar, [http://ismir2009.ismir.net/proceedings/LBD-18.pdf OMRAS2 Metadata Project 2009], Late-breaking session at the 10th International Conference on Music Information Retrieval, 2009.&lt;br /&gt;
&lt;br /&gt;
A. Srinivasamurthy, A. Holzapfel, and Xavier Serra, [http://www.tandfonline.com/doi/full/10.1080/09298215.2013.879902 In Search of Automatic Rhythm Analysis Methods for Turkish and Indian Art Music], Journal of New Music Research, vol. 43, no. 1, pp. 94-114, 2014.&lt;/div&gt;</summary>
		<author><name>Matthew Davies</name></author>
		
	</entry>
	<entry>
		<id>https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10064</id>
		<title>2014:Audio Downbeat Estimation</title>
		<link rel="alternate" type="text/html" href="https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10064"/>
		<updated>2014-05-26T14:43:44Z</updated>

		<summary type="html">&lt;p&gt;Matthew Davies: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Description ==&lt;br /&gt;
&lt;br /&gt;
'''This task is new for 2014!'''&lt;br /&gt;
&lt;br /&gt;
This text has been adapted from the Audio Beat Tracking Wiki page.  Please add your comments and discussion at the bottom of this page.&lt;br /&gt;
&lt;br /&gt;
The aim of the automatic downbeat estimation task is to identify the locations of downbeats in a collection of sound files. While this is similar to the Audio Beat Tracking task, here the aim is to find the first beat of each bar (measure) rather than all beat times. Algorithms are '''not''' required to estimate beat times or time-signature in addition to downbeats.&lt;br /&gt;
&lt;br /&gt;
Submitted algorithms will be evaluated in terms of their accuracy in finding downbeat locations (only) as annotated by musical experts across several diverse datasets.&lt;br /&gt;
&lt;br /&gt;
== Data ==&lt;br /&gt;
&lt;br /&gt;
=== Collections ===&lt;br /&gt;
'''Ballroom'''&lt;br /&gt;
The ballroom dataset contains eight different dance styles (Cha Cha, Jive, Quickstep, Rumba, Samba, Tango, Viennese Waltz and Waltz). It consists of 697 excerpts of 30s in duration.&lt;br /&gt;
The dataset contains two different meters 3/4 and 4/4 - but all pieces have constant meter. For further information see Dixon et al (2004) and Krebs et al (2013).&lt;br /&gt;
Note, we are using the ground truth annotations from Krebs et al. (2013) available at https://github.com/CPJKU/BallroomAnnotations&lt;br /&gt;
&lt;br /&gt;
'''Isophonics (Beatles only)'''&lt;br /&gt;
The Beatles dataset from the Centre for Digital Music at Queen Mary, University of London (http://www.isophonics.net/), as also used for Audio Chord Estimation in MIREX for many years. Available from http://www.isophonics.net/. &lt;br /&gt;
This dataset contains 179 complete songs (all except Revolution 9), the majority of which are in 4/4.&lt;br /&gt;
For further information see Mauch et al (2009).&lt;br /&gt;
&lt;br /&gt;
'''Turkish Data'''&lt;br /&gt;
The Turkish corpus is an extended version of the annotated data used in Srinivasamurthy et al. (2014). It includes 82 excerpts of one&lt;br /&gt;
minute length each, and each piece belongs to one of three&lt;br /&gt;
rhythm classes that are referred to as usul in Turkish Art&lt;br /&gt;
music. 32 pieces are in the 9/8-usul Aksak, 20 pieces&lt;br /&gt;
in the 10/8-usul Curcuna, and 30 samples in the 8/8-usul&lt;br /&gt;
Düyek.&lt;br /&gt;
&lt;br /&gt;
'''Cretan Data'''&lt;br /&gt;
The corpus of Cretan music consists of 42 full length pieces of Cretan leaping dances. While there are several dances that differ in terms of their steps, the differences in&lt;br /&gt;
the sound are most noticeable in the melodic content, and all pieces can be considered to belong to one rhythmic style. All these dances are usually notated using a 2/4 time signature,&lt;br /&gt;
and the accompanying rhythmical patterns are usually played on a Cretan lute. While a variety of rhythmic patterns exist, they do not relate to a specific dance and can be&lt;br /&gt;
assumed to occur in all of the 42 songs in this corpus.&lt;br /&gt;
&lt;br /&gt;
'''Carnatic Data'''&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
'''HJDB''' (to be confirmed)&lt;br /&gt;
236 excerpts of Hardcore, Jungle and Drum and Bass music between 30s and 2 minutes in length. All excerpts are in 4/4 and have a constant tempo. &lt;br /&gt;
For further information see Hockman et al (2012).&lt;br /&gt;
&lt;br /&gt;
In total this makes 1234 excerpts (of which 259 are full length songs).&lt;br /&gt;
&lt;br /&gt;
=== Audio Formats ===&lt;br /&gt;
&lt;br /&gt;
The data are monophonic sound files, with the associated onset times and data about the annotation robustness.&lt;br /&gt;
&lt;br /&gt;
* CD-quality (PCM, 16-bit, 44100 Hz) for all except Ballroom (originally lower quality, but resampled to 44100 Hz)&lt;br /&gt;
* single channel (mono)&lt;br /&gt;
&lt;br /&gt;
== Submission Format ==&lt;br /&gt;
Submissions to this task will have to conform to a specified format detailed below. Submissions should be packaged and contain at least two files: The algorithm itself and a README containing contact information and detailing, in full, the use of the algorithm.&lt;br /&gt;
&lt;br /&gt;
=== Input Data ===&lt;br /&gt;
Participating algorithms will have to read audio in the following format:&lt;br /&gt;
&lt;br /&gt;
* Sample rate: 44.1 KHz&lt;br /&gt;
* Sample size: 16 bit&lt;br /&gt;
* Number of channels: 1 (mono)&lt;br /&gt;
* Encoding: WAV &lt;br /&gt;
&lt;br /&gt;
=== Output Data ===&lt;br /&gt;
&lt;br /&gt;
The downbeat estimation algorithms will return downbeat times in an ASCII text file for each input .wav audio file. The specification of this output file is immediately below.&lt;br /&gt;
&lt;br /&gt;
=== Output File Format (Audio Downbeat Estimation) ===&lt;br /&gt;
&lt;br /&gt;
The downbeat output file format is an ASCII text format. Each downbeat time is specified, in seconds, on its own line. Specifically, &lt;br /&gt;
&lt;br /&gt;
 &amp;lt;downbeat time (in seconds)&amp;gt;\n&lt;br /&gt;
&lt;br /&gt;
where \n denotes the end of line. The &amp;lt; and &amp;gt; characters are not included. An example output file would look something like:&lt;br /&gt;
&lt;br /&gt;
 0.243&lt;br /&gt;
 1.486&lt;br /&gt;
 2.729&lt;br /&gt;
&lt;br /&gt;
=== Algorithm Calling Format ===&lt;br /&gt;
&lt;br /&gt;
The submitted algorithm must take as arguments a SINGLE .wav file to perform the downbeat estimation as well as the full output path and filename of the output file. The ability to specify the output path and file name is essential. Denoting the input .wav file path and name as %input and the output file path and name as %output, a program called foobar could be called from the command-line as follows:&lt;br /&gt;
&lt;br /&gt;
 foobar %input %output&lt;br /&gt;
 foobar -i %input -o %output&lt;br /&gt;
&lt;br /&gt;
Moreover, if your submission takes additional parameters, such as a detection threshold, foobar could be called like:&lt;br /&gt;
&lt;br /&gt;
 foobar .1 %input %output&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output  &lt;br /&gt;
&lt;br /&gt;
If your submission is in MATLAB, it should be submitted as a function. Once again, the function must contain String inputs for the full path and names of the input and output files. Parameters could also be specified as input arguments of the function. For example: &lt;br /&gt;
&lt;br /&gt;
 foobar('%input','%output')&lt;br /&gt;
 foobar(.1,'%input','%output')&lt;br /&gt;
&lt;br /&gt;
=== README File ===&lt;br /&gt;
&lt;br /&gt;
A README file accompanying each submission should contain explicit instructions on how to to run the program (as well as contact information, etc.). In particular, each command line to run should be specified, using %input for the input sound file and %output for the resulting text file.&lt;br /&gt;
&lt;br /&gt;
For instance, to test the program foobar with different values for parameters param1, the README file would look like:&lt;br /&gt;
&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output&lt;br /&gt;
 foobar -param1 .15 -i %input -o %output&lt;br /&gt;
 foobar -param1 .2 -i %input -o %output&lt;br /&gt;
 foobar -param1 .25 -i %input -o %output&lt;br /&gt;
 foobar -param1 .3 -i %input -o %output&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
For a submission using MATLAB, the README file could look like:&lt;br /&gt;
&lt;br /&gt;
 matlab -r &amp;quot;foobar(.1,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.15,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.2,'%input','%output');quit;&amp;quot; &lt;br /&gt;
 matlab -r &amp;quot;foobar(.25,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.3,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
The different command lines to evaluate the performance of each parameter set over the whole database will be generated automatically from each line in the README file containing both '%input' and '%output' strings.&lt;br /&gt;
&lt;br /&gt;
== Evaluation Procedures ==&lt;br /&gt;
&lt;br /&gt;
For the evalution procedure we will use&lt;br /&gt;
*'''F-measure''' - the standard calculation as used in onset and beat tracking evaluation with a +/-70ms window, see Dixon (2007).&lt;br /&gt;
&lt;br /&gt;
Given the high diversity of musical styles included in the task, results will be reported per each invidiual dataset. &lt;br /&gt;
&lt;br /&gt;
== Time and hardware limits ==&lt;br /&gt;
Due to the potentially high number of participants in this and other audio tasks, hard limits on the runtime of submissions will be imposed.&lt;br /&gt;
&lt;br /&gt;
A hard limit of 24 hours will be imposed on analysis times. Submissions exceeding this limit may not receive a result.&lt;br /&gt;
&lt;br /&gt;
== Potential Participants ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
== Discussion ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
= Bibliography =&lt;br /&gt;
&lt;br /&gt;
S. Dixon, F. Gouyon and G. Widmer, [http://ismir2004.ismir.net/proceedings/p093-page-509-paper165.pdf Towards Characterisation of Music via Rhythmic Patterns], In Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR 2004), pp 509-516.&lt;br /&gt;
&lt;br /&gt;
S. Dixon, [http://www.eecs.qmul.ac.uk/~simond/pub/2007/jnmr07.pdf Evaluation of audio beat tracking system BeatRoot], Journal of New Music Research, vol. 36, no. 1, pp. 39-51, 2007.&lt;br /&gt;
&lt;br /&gt;
J. A. Hockman, M. E. P. Davies, I. Fujinaga.[http://ismir2012.ismir.net/event/papers/169-ismir-2012.pdf ONE IN THE JUNGLE: Downbeat Detection in Hardcore, Jungle, and Drum and Bass], In Proceedings of 13th International Society for Music Information Retrieval Conference (ISMIR), Porto, Portugal pp. 169-174, 2012.&lt;br /&gt;
&lt;br /&gt;
F. Krebs, S. Boeck, and G. Widmer, [http://www.cp.jku.at/research/papers/Krebs_etal_ISMIR_2013.pdf Rhythmic Pattern Modeling for Beat- and Downbeat Tracking in Musical Audio], In Proceedings of 14th International Society for Music Information Retrieval Conference (ISMIR), Curitiba, Brazil, 2013.&lt;br /&gt;
&lt;br /&gt;
M. Mauch, C. Cannam, M. E. P. Davies, S. Dixon, C. Harte, S. Kolozali and D. Tidhar, [http://ismir2009.ismir.net/proceedings/LBD-18.pdf OMRAS2 Metadata Project 2009], Late-breaking session at the 10th International Conference on Music Information Retrieval, 2009.&lt;br /&gt;
&lt;br /&gt;
A. Srinivasamurthy, A. Holzapfel, and Xavier Serra, [http://www.tandfonline.com/doi/full/10.1080/09298215.2013.879902 In Search of Automatic Rhythm Analysis Methods for Turkish and Indian Art Music], Journal of New Music Research, vol. 43, no. 1, pp. 94-114, 2014.&lt;/div&gt;</summary>
		<author><name>Matthew Davies</name></author>
		
	</entry>
	<entry>
		<id>https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10063</id>
		<title>2014:Audio Downbeat Estimation</title>
		<link rel="alternate" type="text/html" href="https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10063"/>
		<updated>2014-05-26T14:41:22Z</updated>

		<summary type="html">&lt;p&gt;Matthew Davies: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Description ==&lt;br /&gt;
&lt;br /&gt;
'''This task is new for 2014!'''&lt;br /&gt;
&lt;br /&gt;
This text has been adapted from the Audio Beat Tracking Wiki page.  Please add your comments and discussion at the bottom of this page.&lt;br /&gt;
&lt;br /&gt;
The aim of the automatic downbeat estimation task is to identify the locations of downbeats in a collection of sound files. While this is similar to the Audio Beat Tracking task, here the aim is to find the first beat of each bar (measure) rather than all beat times. Algorithms are '''not''' required to estimate beat times or time-signature in addition to downbeats.&lt;br /&gt;
&lt;br /&gt;
Submitted algorithms will be evaluated in terms of their accuracy in finding downbeat locations (only) as annotated by musical experts across several diverse datasets.&lt;br /&gt;
&lt;br /&gt;
== Data ==&lt;br /&gt;
&lt;br /&gt;
=== Collections ===&lt;br /&gt;
'''Ballroom'''&lt;br /&gt;
The ballroom dataset contains eight different dance styles (Cha Cha, Jive, Quickstep, Rumba, Samba, Tango, Viennese Waltz and Waltz). It consists of 697 excerpts of 30s in duration.&lt;br /&gt;
The dataset contains two different meters 3/4 and 4/4 - but all pieces have constant meter. For further information see Dixon et al (2004) and Krebs et al (2013).&lt;br /&gt;
Note, we are using the ground truth annotations from Krebs et al. (2013) available at https://github.com/CPJKU/BallroomAnnotations&lt;br /&gt;
&lt;br /&gt;
'''Isophonics (Beatles only)'''&lt;br /&gt;
The Beatles dataset from the Centre for Digital Music at Queen Mary, University of London (http://www.isophonics.net/), as also used for Audio Chord Estimation in MIREX for many years. Available from http://www.isophonics.net/. &lt;br /&gt;
This dataset contains 179 complete songs (all except Revolution 9), the majority of which are in 4/4.&lt;br /&gt;
For further information see Mauch et al (2009).&lt;br /&gt;
&lt;br /&gt;
'''Turkish Data'''&lt;br /&gt;
The Turkish corpus is an extended version of the annotated data used in Srinivasamurthy et al. (2014). It includes 82 excerpts of one&lt;br /&gt;
minute length each, and each piece belongs to one of three&lt;br /&gt;
rhythm classes that are referred to as usul in Turkish Art&lt;br /&gt;
music. 32 pieces are in the 9/8-usul Aksak, 20 pieces&lt;br /&gt;
in the 10/8-usul Curcuna, and 30 samples in the 8/8-usul&lt;br /&gt;
Düyek.&lt;br /&gt;
&lt;br /&gt;
'''Cretan Data'''&lt;br /&gt;
The corpus of Cretan music consists of 42 full length pieces of Cretan leaping dances. While there are several dances that differ in terms of their steps, the differences in&lt;br /&gt;
the sound are most noticeable in the melodic content, and all pieces can be considered to belong to one rhythmic style. All these dances are usually notated using a 2/4 time signature,&lt;br /&gt;
and the accompanying rhythmical patterns are usually played on a Cretan lute. While a variety of rhythmic patterns exist, they do not relate to a specific dance and can be&lt;br /&gt;
assumed to occur in all of the 42 songs in this corpus.&lt;br /&gt;
&lt;br /&gt;
'''Carnatic Data'''&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
'''HJDB''' (to be confirmed)&lt;br /&gt;
236 excerpts of Hardcore, Jungle and Drum and Bass music between 30s and 2 minutes in length. All excerpts are in 4/4 and have a constant tempo. &lt;br /&gt;
For further information see Hockman et al (2012).&lt;br /&gt;
&lt;br /&gt;
In total this makes 1234 excerpts (of which 259 are full length songs).&lt;br /&gt;
&lt;br /&gt;
=== Audio Formats ===&lt;br /&gt;
&lt;br /&gt;
The data are monophonic sound files, with the associated onset times and data about the annotation robustness.&lt;br /&gt;
&lt;br /&gt;
* CD-quality (PCM, 16-bit, 44100 Hz) for all except Ballroom (originally lower quality, but resampled to 44100 Hz)&lt;br /&gt;
* single channel (mono)&lt;br /&gt;
&lt;br /&gt;
== Submission Format ==&lt;br /&gt;
Submissions to this task will have to conform to a specified format detailed below. Submissions should be packaged and contain at least two files: The algorithm itself and a README containing contact information and detailing, in full, the use of the algorithm.&lt;br /&gt;
&lt;br /&gt;
=== Input Data ===&lt;br /&gt;
Participating algorithms will have to read audio in the following format:&lt;br /&gt;
&lt;br /&gt;
* Sample rate: 44.1 KHz&lt;br /&gt;
* Sample size: 16 bit&lt;br /&gt;
* Number of channels: 1 (mono)&lt;br /&gt;
* Encoding: WAV &lt;br /&gt;
&lt;br /&gt;
=== Output Data ===&lt;br /&gt;
&lt;br /&gt;
The downbeat estimation algorithms will return downbeat times in an ASCII text file for each input .wav audio file. The specification of this output file is immediately below.&lt;br /&gt;
&lt;br /&gt;
=== Output File Format (Audio Downbeat Estimation) ===&lt;br /&gt;
&lt;br /&gt;
The downbeat output file format is an ASCII text format. Each downbeat time is specified, in seconds, on its own line. Specifically, &lt;br /&gt;
&lt;br /&gt;
 &amp;lt;downbeat time (in seconds)&amp;gt;\n&lt;br /&gt;
&lt;br /&gt;
where \n denotes the end of line. The &amp;lt; and &amp;gt; characters are not included. An example output file would look something like:&lt;br /&gt;
&lt;br /&gt;
 0.243&lt;br /&gt;
 1.486&lt;br /&gt;
 2.729&lt;br /&gt;
&lt;br /&gt;
=== Algorithm Calling Format ===&lt;br /&gt;
&lt;br /&gt;
The submitted algorithm must take as arguments a SINGLE .wav file to perform the downbeat estimation as well as the full output path and filename of the output file. The ability to specify the output path and file name is essential. Denoting the input .wav file path and name as %input and the output file path and name as %output, a program called foobar could be called from the command-line as follows:&lt;br /&gt;
&lt;br /&gt;
 foobar %input %output&lt;br /&gt;
 foobar -i %input -o %output&lt;br /&gt;
&lt;br /&gt;
Moreover, if your submission takes additional parameters, such as a detection threshold, foobar could be called like:&lt;br /&gt;
&lt;br /&gt;
 foobar .1 %input %output&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output  &lt;br /&gt;
&lt;br /&gt;
If your submission is in MATLAB, it should be submitted as a function. Once again, the function must contain String inputs for the full path and names of the input and output files. Parameters could also be specified as input arguments of the function. For example: &lt;br /&gt;
&lt;br /&gt;
 foobar('%input','%output')&lt;br /&gt;
 foobar(.1,'%input','%output')&lt;br /&gt;
&lt;br /&gt;
=== README File ===&lt;br /&gt;
&lt;br /&gt;
A README file accompanying each submission should contain explicit instructions on how to to run the program (as well as contact information, etc.). In particular, each command line to run should be specified, using %input for the input sound file and %output for the resulting text file.&lt;br /&gt;
&lt;br /&gt;
For instance, to test the program foobar with different values for parameters param1, the README file would look like:&lt;br /&gt;
&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output&lt;br /&gt;
 foobar -param1 .15 -i %input -o %output&lt;br /&gt;
 foobar -param1 .2 -i %input -o %output&lt;br /&gt;
 foobar -param1 .25 -i %input -o %output&lt;br /&gt;
 foobar -param1 .3 -i %input -o %output&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
For a submission using MATLAB, the README file could look like:&lt;br /&gt;
&lt;br /&gt;
 matlab -r &amp;quot;foobar(.1,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.15,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.2,'%input','%output');quit;&amp;quot; &lt;br /&gt;
 matlab -r &amp;quot;foobar(.25,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.3,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
The different command lines to evaluate the performance of each parameter set over the whole database will be generated automatically from each line in the README file containing both '%input' and '%output' strings.&lt;br /&gt;
&lt;br /&gt;
== Evaluation Procedures ==&lt;br /&gt;
&lt;br /&gt;
For the evalution procedure we will use&lt;br /&gt;
*'''F-measure''' - the standard calculation as used in onset and beat tracking evaluation with a +/-70ms window, see Dixon (2007).&lt;br /&gt;
&lt;br /&gt;
Given the high diversity of musical styles included in the task, results will be reported per each invidiual dataset. &lt;br /&gt;
&lt;br /&gt;
== Time and hardware limits ==&lt;br /&gt;
Due to the potentially high number of participants in this and other audio tasks, hard limits on the runtime of submissions will be imposed.&lt;br /&gt;
&lt;br /&gt;
A hard limit of 24 hours will be imposed on analysis times. Submissions exceeding this limit may not receive a result.&lt;br /&gt;
&lt;br /&gt;
== Potential Participants ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
== Discussion ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
= Bibliography =&lt;br /&gt;
&lt;br /&gt;
S. Dixon, F. Gouyon and G. Widmer, [http://ismir2004.ismir.net/proceedings/p093-page-509-paper165.pdf Towards Characterisation of Music via Rhythmic Patterns], In Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR 2004), pp 509-516.&lt;br /&gt;
&lt;br /&gt;
S. Dixon, &amp;quot;Evaluation of audio beat tracking system beatroot,&amp;quot; Journal of New Music Research, vol. 36, no. 1, pp. 39-51, 2007.&lt;br /&gt;
&lt;br /&gt;
J. A. Hockman, M. E. P. Davies, I. Fujinaga.[http://ismir2012.ismir.net/event/papers/169-ismir-2012.pdf ONE IN THE JUNGLE: Downbeat Detection in Hardcore, Jungle, and Drum and Bass], In Proceedings of 13th International Society for Music Information Retrieval Conference (ISMIR), Porto, Portugal pp. 169-174, 2012.&lt;br /&gt;
&lt;br /&gt;
F. Krebs, S. Boeck, and G. Widmer, [http://www.cp.jku.at/research/papers/Krebs_etal_ISMIR_2013.pdf Rhythmic Pattern Modeling for Beat- and Downbeat Tracking in Musical Audio], In Proceedings of 14th International Society for Music Information Retrieval Conference (ISMIR), Curitiba, Brazil, 2013.&lt;br /&gt;
&lt;br /&gt;
M. Mauch, C. Cannam, M. E. P. Davies, S. Dixon, C. Harte, S. Kolozali and D. Tidhar, &amp;quot;OMRAS2 Metadata Project 2009&amp;quot;, Late-breaking session at the 10th International Conference on Music Information Retrieval, 2009.&lt;br /&gt;
&lt;br /&gt;
A. Srinivasamurthy, A. Holzapfel, and Xavier Serra, &amp;quot;In Search of Automatic Rhythm Analysis Methods for Turkish and Indian Art Music&amp;quot;, Journal of New Music Research, vol. 43, no. 1, pp. 94-114, 2014.&lt;/div&gt;</summary>
		<author><name>Matthew Davies</name></author>
		
	</entry>
	<entry>
		<id>https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10062</id>
		<title>2014:Audio Downbeat Estimation</title>
		<link rel="alternate" type="text/html" href="https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10062"/>
		<updated>2014-05-26T14:27:53Z</updated>

		<summary type="html">&lt;p&gt;Matthew Davies: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Description ==&lt;br /&gt;
&lt;br /&gt;
'''This task is new for 2014!'''&lt;br /&gt;
&lt;br /&gt;
This text has been adapted from the Audio Beat Tracking Wiki page.  Please add your comments and discussion at the bottom of this page.&lt;br /&gt;
&lt;br /&gt;
The aim of the automatic downbeat estimation task is to identify the locations of downbeats in a collection of sound files. While this is similar to the Audio Beat Tracking task, here the aim is to find the first beat of each bar (measure) rather than all beat times. Algorithms are '''not''' required to estimate beat times or time-signature in addition to downbeats.&lt;br /&gt;
&lt;br /&gt;
Submitted algorithms will be evaluated in terms of their accuracy in finding downbeat locations (only) as annotated by musical experts across several diverse datasets.&lt;br /&gt;
&lt;br /&gt;
== Data ==&lt;br /&gt;
&lt;br /&gt;
=== Collections ===&lt;br /&gt;
'''Ballroom'''&lt;br /&gt;
The ballroom dataset contains eight different dance styles (Cha Cha, Jive, Quickstep, Rumba, Samba, Tango, Viennese Waltz and Waltz). It consists of 697 excerpts of 30s in duration.&lt;br /&gt;
The dataset contains two different meters 3/4 and 4/4 - but all pieces have constant meter. For further information see Dixon et al (2004) and Krebs et al (2013).&lt;br /&gt;
Note, we are using the ground truth annotations from Krebs et al. (2013) available at https://github.com/CPJKU/BallroomAnnotations&lt;br /&gt;
&lt;br /&gt;
'''Isophonics (Beatles only)'''&lt;br /&gt;
The Beatles dataset from the Centre for Digital Music at Queen Mary, University of London (http://www.isophonics.net/), as also used for Audio Chord Estimation in MIREX for many years. Available from http://www.isophonics.net/. &lt;br /&gt;
This dataset contains 179 complete songs (all except Revolution 9), the majority of which are in 4/4.&lt;br /&gt;
For further information see Mauch et al (2009).&lt;br /&gt;
&lt;br /&gt;
'''Turkish Data'''&lt;br /&gt;
The Turkish corpus is an extended version of the annotated data used in Srinivasamurthy et al. (2014). It includes 82 excerpts of one&lt;br /&gt;
minute length each, and each piece belongs to one of three&lt;br /&gt;
rhythm classes that are referred to as usul in Turkish Art&lt;br /&gt;
music. 32 pieces are in the 9/8-usul Aksak, 20 pieces&lt;br /&gt;
in the 10/8-usul Curcuna, and 30 samples in the 8/8-usul&lt;br /&gt;
Düyek.&lt;br /&gt;
&lt;br /&gt;
'''Cretan Data'''&lt;br /&gt;
The corpus of Cretan music consists of 42 full length pieces of Cretan leaping dances. While there are several dances that differ in terms of their steps, the differences in&lt;br /&gt;
the sound are most noticeable in the melodic content, and all pieces can be considered to belong to one rhythmic style. All these dances are usually notated using a 2/4 time signature,&lt;br /&gt;
and the accompanying rhythmical patterns are usually played on a Cretan lute. While a variety of rhythmic patterns exist, they do not relate to a specific dance and can be&lt;br /&gt;
assumed to occur in all of the 42 songs in this corpus.&lt;br /&gt;
&lt;br /&gt;
'''Carnatic Data'''&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
'''HJDB''' (to be confirmed)&lt;br /&gt;
236 excerpts of Hardcore, Jungle and Drum and Bass music between 30s and 2 minutes in length. All excerpts are in 4/4 and have a constant tempo. &lt;br /&gt;
For further information see Hockman et al (2012).&lt;br /&gt;
&lt;br /&gt;
In total this makes 1234 excerpts (of which 259 are full length songs).&lt;br /&gt;
&lt;br /&gt;
=== Audio Formats ===&lt;br /&gt;
&lt;br /&gt;
The data are monophonic sound files, with the associated onset times and data about the annotation robustness.&lt;br /&gt;
&lt;br /&gt;
* CD-quality (PCM, 16-bit, 44100 Hz) for all except Ballroom (originally lower quality, but resampled to 44100 Hz)&lt;br /&gt;
* single channel (mono)&lt;br /&gt;
&lt;br /&gt;
== Submission Format ==&lt;br /&gt;
Submissions to this task will have to conform to a specified format detailed below. Submissions should be packaged and contain at least two files: The algorithm itself and a README containing contact information and detailing, in full, the use of the algorithm.&lt;br /&gt;
&lt;br /&gt;
=== Input Data ===&lt;br /&gt;
Participating algorithms will have to read audio in the following format:&lt;br /&gt;
&lt;br /&gt;
* Sample rate: 44.1 KHz&lt;br /&gt;
* Sample size: 16 bit&lt;br /&gt;
* Number of channels: 1 (mono)&lt;br /&gt;
* Encoding: WAV &lt;br /&gt;
&lt;br /&gt;
=== Output Data ===&lt;br /&gt;
&lt;br /&gt;
The downbeat estimation algorithms will return downbeat times in an ASCII text file for each input .wav audio file. The specification of this output file is immediately below.&lt;br /&gt;
&lt;br /&gt;
=== Output File Format (Audio Downbeat Estimation) ===&lt;br /&gt;
&lt;br /&gt;
The downbeat output file format is an ASCII text format. Each downbeat time is specified, in seconds, on its own line. Specifically, &lt;br /&gt;
&lt;br /&gt;
 &amp;lt;downbeat time (in seconds)&amp;gt;\n&lt;br /&gt;
&lt;br /&gt;
where \n denotes the end of line. The &amp;lt; and &amp;gt; characters are not included. An example output file would look something like:&lt;br /&gt;
&lt;br /&gt;
 0.243&lt;br /&gt;
 1.486&lt;br /&gt;
 2.729&lt;br /&gt;
&lt;br /&gt;
=== Algorithm Calling Format ===&lt;br /&gt;
&lt;br /&gt;
The submitted algorithm must take as arguments a SINGLE .wav file to perform the downbeat estimation as well as the full output path and filename of the output file. The ability to specify the output path and file name is essential. Denoting the input .wav file path and name as %input and the output file path and name as %output, a program called foobar could be called from the command-line as follows:&lt;br /&gt;
&lt;br /&gt;
 foobar %input %output&lt;br /&gt;
 foobar -i %input -o %output&lt;br /&gt;
&lt;br /&gt;
Moreover, if your submission takes additional parameters, such as a detection threshold, foobar could be called like:&lt;br /&gt;
&lt;br /&gt;
 foobar .1 %input %output&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output  &lt;br /&gt;
&lt;br /&gt;
If your submission is in MATLAB, it should be submitted as a function. Once again, the function must contain String inputs for the full path and names of the input and output files. Parameters could also be specified as input arguments of the function. For example: &lt;br /&gt;
&lt;br /&gt;
 foobar('%input','%output')&lt;br /&gt;
 foobar(.1,'%input','%output')&lt;br /&gt;
&lt;br /&gt;
=== README File ===&lt;br /&gt;
&lt;br /&gt;
A README file accompanying each submission should contain explicit instructions on how to to run the program (as well as contact information, etc.). In particular, each command line to run should be specified, using %input for the input sound file and %output for the resulting text file.&lt;br /&gt;
&lt;br /&gt;
For instance, to test the program foobar with different values for parameters param1, the README file would look like:&lt;br /&gt;
&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output&lt;br /&gt;
 foobar -param1 .15 -i %input -o %output&lt;br /&gt;
 foobar -param1 .2 -i %input -o %output&lt;br /&gt;
 foobar -param1 .25 -i %input -o %output&lt;br /&gt;
 foobar -param1 .3 -i %input -o %output&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
For a submission using MATLAB, the README file could look like:&lt;br /&gt;
&lt;br /&gt;
 matlab -r &amp;quot;foobar(.1,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.15,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.2,'%input','%output');quit;&amp;quot; &lt;br /&gt;
 matlab -r &amp;quot;foobar(.25,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.3,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
The different command lines to evaluate the performance of each parameter set over the whole database will be generated automatically from each line in the README file containing both '%input' and '%output' strings.&lt;br /&gt;
&lt;br /&gt;
== Evaluation Procedures ==&lt;br /&gt;
&lt;br /&gt;
For the evalution procedure we will use&lt;br /&gt;
*'''F-measure''' - the standard calculation as used in onset and beat tracking evaluation with a +/-70ms window, see Dixon (2007).&lt;br /&gt;
&lt;br /&gt;
Given the high diversity of musical styles included in the task, results will be reported per each invidiual dataset. &lt;br /&gt;
&lt;br /&gt;
== Time and hardware limits ==&lt;br /&gt;
Due to the potentially high number of participants in this and other audio tasks, hard limits on the runtime of submissions will be imposed.&lt;br /&gt;
&lt;br /&gt;
A hard limit of 24 hours will be imposed on analysis times. Submissions exceeding this limit may not receive a result.&lt;br /&gt;
&lt;br /&gt;
== Potential Participants ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
== Discussion ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
= Bibliography =&lt;br /&gt;
S. Dixon, F. Gouyon and G. Widmer, &amp;quot;Towards Characterisation of Music via Rhythmic Patterns&amp;quot;, In Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR 2004), pp 509-516.&lt;br /&gt;
&lt;br /&gt;
S. Dixon, &amp;quot;Evaluation of audio beat tracking system beatroot,&amp;quot; Journal of New Music Research, vol. 36, no. 1, pp. 39-51, 2007.&lt;br /&gt;
&lt;br /&gt;
J. A. Hockman, M. E. P. Davies, I. Fujinaga.&amp;quot;ONE IN THE JUNGLE: Downbeat Detection in Hardcore, Jungle, and Drum and Bass&amp;quot;, In Proceedings of 13th International Society for Music Information Retrieval Conference (ISMIR), Porto, Portugal pp. 169-174, 2012.&lt;br /&gt;
&lt;br /&gt;
F. Krebs, S. Boeck, and G. Widmer, &amp;quot;Rhythmic Pattern Modeling for Beat- and Downbeat Tracking in Musical Audio&amp;quot;, In Proceedings of 14th International Society for Music Information Retrieval Conference (ISMIR), Curitiba, Brazil, 2013.&lt;br /&gt;
&lt;br /&gt;
M. Mauch, C. Cannam, M. E. P. Davies, S. Dixon, C. Harte, S. Kolozali and D. Tidhar, &amp;quot;OMRAS2 Metadata Project 2009&amp;quot;, Late-breaking session at the 10th International Conference on Music Information Retrieval, 2009.&lt;br /&gt;
&lt;br /&gt;
A. Srinivasamurthy, A. Holzapfel, and Xavier Serra, &amp;quot;In Search of Automatic Rhythm Analysis Methods for Turkish and Indian Art Music&amp;quot;, Journal of New Music Research, vol. 43, no. 1, pp. 94-114, 2014.&lt;/div&gt;</summary>
		<author><name>Matthew Davies</name></author>
		
	</entry>
	<entry>
		<id>https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10061</id>
		<title>2014:Audio Downbeat Estimation</title>
		<link rel="alternate" type="text/html" href="https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10061"/>
		<updated>2014-05-26T14:27:21Z</updated>

		<summary type="html">&lt;p&gt;Matthew Davies: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Description ==&lt;br /&gt;
&lt;br /&gt;
'''This task is new for 2014!'''&lt;br /&gt;
&lt;br /&gt;
This text has been adapted from the Audio Beat Tracking Wiki page.  Please add your comments and discussion at the bottom of this page.&lt;br /&gt;
&lt;br /&gt;
The aim of the automatic downbeat estimation task is to identify the locations of downbeats in a collection of sound files. While this is similar to the Audio Beat Tracking task, here the aim is to find the first beat of each bar (measure) rather than all beat times. Algorithms are '''not''' required to estimate beat times or time-signature in addition to downbeats.&lt;br /&gt;
&lt;br /&gt;
Submitted algorithms will be evaluated in terms of their accuracy in finding downbeat locations (only) as annotated by musical experts across several diverse datasets.&lt;br /&gt;
&lt;br /&gt;
== Data ==&lt;br /&gt;
&lt;br /&gt;
=== Collections ===&lt;br /&gt;
'''Ballroom'''&lt;br /&gt;
The ballroom dataset contains eight different dance styles (Cha Cha, Jive, Quickstep, Rumba, Samba, Tango, Viennese Waltz and Waltz). It consists of 697 excerpts of 30s in duration.&lt;br /&gt;
The dataset contains two different meters 3/4 and 4/4 - but all pieces have constant meter. For further information see Dixon et al (2004) and Krebs et al (2013).&lt;br /&gt;
Note, we are using the ground truth annotations from Krebs et al. (2013) available at https://github.com/CPJKU/BallroomAnnotations&lt;br /&gt;
&lt;br /&gt;
'''Isophonics (Beatles only)'''&lt;br /&gt;
The Beatles dataset from the Centre for Digital Music at Queen Mary, University of London (http://www.isophonics.net/), as also used for Audio Chord Estimation in MIREX for many years. Available from http://www.isophonics.net/. &lt;br /&gt;
This dataset contains 179 complete songs (all except Revolution 9), the majority of which are in 4/4.&lt;br /&gt;
For further information see Mauch et al (2009).&lt;br /&gt;
&lt;br /&gt;
'''Turkish Data'''&lt;br /&gt;
The Turkish corpus is an extended version of the annotated data used in Srinivasamurthy et al. (2014). It includes 82 excerpts of one&lt;br /&gt;
minute length each, and each piece belongs to one of three&lt;br /&gt;
rhythm classes that are referred to as usul in Turkish Art&lt;br /&gt;
music. 32 pieces are in the 9/8-usul Aksak, 20 pieces&lt;br /&gt;
in the 10/8-usul Curcuna, and 30 samples in the 8/8-usul&lt;br /&gt;
Düyek.&lt;br /&gt;
&lt;br /&gt;
'''Cretan Data'''&lt;br /&gt;
The corpus of Cretan music consists of 42 full length pieces of Cretan leaping dances. While there are several dances that differ in terms of their steps, the differences in&lt;br /&gt;
the sound are most noticeable in the melodic content, and all pieces can be considered to belong to one rhythmic style. All these dances are usually notated using a 2/4 time signature,&lt;br /&gt;
and the accompanying rhythmical patterns are usually played on a Cretan lute. While a variety of rhythmic patterns exist, they do not relate to a specific dance and can be&lt;br /&gt;
assumed to occur in all of the 42 songs in this corpus.&lt;br /&gt;
&lt;br /&gt;
'''Carnatic Data'''&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
'''HJDB''' (to be confirmed)&lt;br /&gt;
236 excerpts of Hardcore, Jungle and Drum and Bass music between 30s and 2 minutes in length. All excerpts are in 4/4 and have a constant tempo. &lt;br /&gt;
For further information see Hockman et al (2012).&lt;br /&gt;
&lt;br /&gt;
In total this makes 1234 excerpts (of which 259 are full length songs).&lt;br /&gt;
&lt;br /&gt;
=== Audio Formats ===&lt;br /&gt;
&lt;br /&gt;
The data are monophonic sound files, with the associated onset times and data about the annotation robustness.&lt;br /&gt;
&lt;br /&gt;
* CD-quality (PCM, 16-bit, 44100 Hz) for all except Ballroom (originally lower quality, but resampled to 44100 Hz)&lt;br /&gt;
* single channel (mono)&lt;br /&gt;
&lt;br /&gt;
== Submission Format ==&lt;br /&gt;
Submissions to this task will have to conform to a specified format detailed below. Submissions should be packaged and contain at least two files: The algorithm itself and a README containing contact information and detailing, in full, the use of the algorithm.&lt;br /&gt;
&lt;br /&gt;
=== Input Data ===&lt;br /&gt;
Participating algorithms will have to read audio in the following format:&lt;br /&gt;
&lt;br /&gt;
* Sample rate: 44.1 KHz&lt;br /&gt;
* Sample size: 16 bit&lt;br /&gt;
* Number of channels: 1 (mono)&lt;br /&gt;
* Encoding: WAV &lt;br /&gt;
&lt;br /&gt;
=== Output Data ===&lt;br /&gt;
&lt;br /&gt;
The downbeat estimation algorithms will return downbeat times in an ASCII text file for each input .wav audio file. The specification of this output file is immediately below.&lt;br /&gt;
&lt;br /&gt;
=== Output File Format (Audio Downbeat Estimation) ===&lt;br /&gt;
&lt;br /&gt;
The downbeat output file format is an ASCII text format. Each downbeat time is specified, in seconds, on its own line. Specifically, &lt;br /&gt;
&lt;br /&gt;
 &amp;lt;downbeat time (in seconds)&amp;gt;\n&lt;br /&gt;
&lt;br /&gt;
where \n denotes the end of line. The &amp;lt; and &amp;gt; characters are not included. An example output file would look something like:&lt;br /&gt;
&lt;br /&gt;
 0.243&lt;br /&gt;
 1.486&lt;br /&gt;
 2.729&lt;br /&gt;
&lt;br /&gt;
=== Algorithm Calling Format ===&lt;br /&gt;
&lt;br /&gt;
The submitted algorithm must take as arguments a SINGLE .wav file to perform the downbeat estimation as well as the full output path and filename of the output file. The ability to specify the output path and file name is essential. Denoting the input .wav file path and name as %input and the output file path and name as %output, a program called foobar could be called from the command-line as follows:&lt;br /&gt;
&lt;br /&gt;
 foobar %input %output&lt;br /&gt;
 foobar -i %input -o %output&lt;br /&gt;
&lt;br /&gt;
Moreover, if your submission takes additional parameters, such as a detection threshold, foobar could be called like:&lt;br /&gt;
&lt;br /&gt;
 foobar .1 %input %output&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output  &lt;br /&gt;
&lt;br /&gt;
If your submission is in MATLAB, it should be submitted as a function. Once again, the function must contain String inputs for the full path and names of the input and output files. Parameters could also be specified as input arguments of the function. For example: &lt;br /&gt;
&lt;br /&gt;
 foobar('%input','%output')&lt;br /&gt;
 foobar(.1,'%input','%output')&lt;br /&gt;
&lt;br /&gt;
=== README File ===&lt;br /&gt;
&lt;br /&gt;
A README file accompanying each submission should contain explicit instructions on how to to run the program (as well as contact information, etc.). In particular, each command line to run should be specified, using %input for the input sound file and %output for the resulting text file.&lt;br /&gt;
&lt;br /&gt;
For instance, to test the program foobar with different values for parameters param1, the README file would look like:&lt;br /&gt;
&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output&lt;br /&gt;
 foobar -param1 .15 -i %input -o %output&lt;br /&gt;
 foobar -param1 .2 -i %input -o %output&lt;br /&gt;
 foobar -param1 .25 -i %input -o %output&lt;br /&gt;
 foobar -param1 .3 -i %input -o %output&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
For a submission using MATLAB, the README file could look like:&lt;br /&gt;
&lt;br /&gt;
 matlab -r &amp;quot;foobar(.1,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.15,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.2,'%input','%output');quit;&amp;quot; &lt;br /&gt;
 matlab -r &amp;quot;foobar(.25,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.3,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
The different command lines to evaluate the performance of each parameter set over the whole database will be generated automatically from each line in the README file containing both '%input' and '%output' strings.&lt;br /&gt;
&lt;br /&gt;
== Evaluation Procedures ==&lt;br /&gt;
&lt;br /&gt;
For the evalution procedure we will use&lt;br /&gt;
*'''F-measure''' - the standard calculation as used in onset and beat tracking evaluation with a +/-70ms window, see Dixon (2007).&lt;br /&gt;
&lt;br /&gt;
Given the high diversity of musical styles included in the task, results will be reported per dataset. &lt;br /&gt;
&lt;br /&gt;
== Time and hardware limits ==&lt;br /&gt;
Due to the potentially high number of participants in this and other audio tasks, hard limits on the runtime of submissions will be imposed.&lt;br /&gt;
&lt;br /&gt;
A hard limit of 24 hours will be imposed on analysis times. Submissions exceeding this limit may not receive a result.&lt;br /&gt;
&lt;br /&gt;
== Potential Participants ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
== Discussion ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
= Bibliography =&lt;br /&gt;
S. Dixon, F. Gouyon and G. Widmer, &amp;quot;Towards Characterisation of Music via Rhythmic Patterns&amp;quot;, In Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR 2004), pp 509-516.&lt;br /&gt;
&lt;br /&gt;
S. Dixon, &amp;quot;Evaluation of audio beat tracking system beatroot,&amp;quot; Journal of New Music Research, vol. 36, no. 1, pp. 39-51, 2007.&lt;br /&gt;
&lt;br /&gt;
J. A. Hockman, M. E. P. Davies, I. Fujinaga.&amp;quot;ONE IN THE JUNGLE: Downbeat Detection in Hardcore, Jungle, and Drum and Bass&amp;quot;, In Proceedings of 13th International Society for Music Information Retrieval Conference (ISMIR), Porto, Portugal pp. 169-174, 2012.&lt;br /&gt;
&lt;br /&gt;
F. Krebs, S. Boeck, and G. Widmer, &amp;quot;Rhythmic Pattern Modeling for Beat- and Downbeat Tracking in Musical Audio&amp;quot;, In Proceedings of 14th International Society for Music Information Retrieval Conference (ISMIR), Curitiba, Brazil, 2013.&lt;br /&gt;
&lt;br /&gt;
M. Mauch, C. Cannam, M. E. P. Davies, S. Dixon, C. Harte, S. Kolozali and D. Tidhar, &amp;quot;OMRAS2 Metadata Project 2009&amp;quot;, Late-breaking session at the 10th International Conference on Music Information Retrieval, 2009.&lt;br /&gt;
&lt;br /&gt;
A. Srinivasamurthy, A. Holzapfel, and Xavier Serra, &amp;quot;In Search of Automatic Rhythm Analysis Methods for Turkish and Indian Art Music&amp;quot;, Journal of New Music Research, vol. 43, no. 1, pp. 94-114, 2014.&lt;/div&gt;</summary>
		<author><name>Matthew Davies</name></author>
		
	</entry>
	<entry>
		<id>https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10060</id>
		<title>2014:Audio Downbeat Estimation</title>
		<link rel="alternate" type="text/html" href="https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10060"/>
		<updated>2014-05-26T14:24:38Z</updated>

		<summary type="html">&lt;p&gt;Matthew Davies: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Description ==&lt;br /&gt;
&lt;br /&gt;
'''This task is new for 2014!'''&lt;br /&gt;
&lt;br /&gt;
This text has been adapted from the Audio Beat Tracking Wiki page.  Please add your comments and discussion at the bottom of this page.&lt;br /&gt;
&lt;br /&gt;
The aim of the automatic downbeat estimation task is to identify the locations of downbeats in a collection of sound files. While this is similar to the Audio Beat Tracking task, here the aim is to find the first beat of each bar (measure) rather than all beat times. Algorithms are '''not''' required to estimate beat times or time-signature in addition to downbeats.&lt;br /&gt;
&lt;br /&gt;
Submitted algorithms will be evaluated in terms of their accuracy in finding downbeat locations (only) as annotated by musical experts across several diverse datasets.&lt;br /&gt;
&lt;br /&gt;
== Data ==&lt;br /&gt;
&lt;br /&gt;
=== Collections ===&lt;br /&gt;
'''Ballroom'''&lt;br /&gt;
The ballroom dataset contains eight different dance styles (Cha Cha, Jive, Quickstep, Rumba, Samba, Tango, Viennese Waltz and Waltz). It consists of 697 excerpts of 30s in duration.&lt;br /&gt;
The dataset contains two different meters 3/4 and 4/4 - but all pieces have constant meter. For further information see Dixon et al (2004) and Krebs et al (2013).&lt;br /&gt;
Note, we are using the ground truth annotations from Krebs et al. (2013) available at https://github.com/CPJKU/BallroomAnnotations&lt;br /&gt;
&lt;br /&gt;
'''Isophonics (Beatles only)'''&lt;br /&gt;
The Beatles dataset from the Centre for Digital Music at Queen Mary, University of London (http://www.isophonics.net/), as also used for Audio Chord Estimation in MIREX for many years. Available from http://www.isophonics.net/. &lt;br /&gt;
This dataset contains 179 complete songs (all except Revolution 9), the majority of which are in 4/4.&lt;br /&gt;
For further information see Mauch et al (2009).&lt;br /&gt;
&lt;br /&gt;
'''Turkish Data'''&lt;br /&gt;
The Turkish corpus is an extended version of the annotated data used in Srinivasamurthy et al. (2014). It includes 82 excerpts of one&lt;br /&gt;
minute length each, and each piece belongs to one of three&lt;br /&gt;
rhythm classes that are referred to as usul in Turkish Art&lt;br /&gt;
music. 32 pieces are in the 9/8-usul Aksak, 20 pieces&lt;br /&gt;
in the 10/8-usul Curcuna, and 30 samples in the 8/8-usul&lt;br /&gt;
Düyek.&lt;br /&gt;
&lt;br /&gt;
'''Cretan Data'''&lt;br /&gt;
The corpus of Cretan music consists of 42 full length pieces of Cretan leaping dances. While there are several dances that differ in terms of their steps, the differences in&lt;br /&gt;
the sound are most noticeable in the melodic content, and all pieces can be considered to belong to one rhythmic style. All these dances are usually notated using a 2/4 time signature,&lt;br /&gt;
and the accompanying rhythmical patterns are usually played on a Cretan lute. While a variety of rhythmic patterns exist, they do not relate to a specific dance and can be&lt;br /&gt;
assumed to occur in all of the 42 songs in this corpus.&lt;br /&gt;
&lt;br /&gt;
'''Carnatic Data'''&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
'''HJDB''' (to be confirmed)&lt;br /&gt;
236 excerpts of Hardcore, Jungle and Drum and Bass music between 30s and 2 minutes in length. All excerpts are in 4/4 and have a constant tempo. &lt;br /&gt;
For further information see Hockman et al (2012).&lt;br /&gt;
&lt;br /&gt;
In total this makes 1234 excerpts (of which 259 are full length songs).&lt;br /&gt;
&lt;br /&gt;
=== Audio Formats ===&lt;br /&gt;
&lt;br /&gt;
The data are monophonic sound files, with the associated onset times and data about the annotation robustness.&lt;br /&gt;
&lt;br /&gt;
* CD-quality (PCM, 16-bit, 44100 Hz) for all except Ballroom (originally lower quality, but resampled to 44100 Hz)&lt;br /&gt;
* single channel (mono)&lt;br /&gt;
&lt;br /&gt;
== Submission Format ==&lt;br /&gt;
Submissions to this task will have to conform to a specified format detailed below. Submissions should be packaged and contain at least two files: The algorithm itself and a README containing contact information and detailing, in full, the use of the algorithm.&lt;br /&gt;
&lt;br /&gt;
=== Input Data ===&lt;br /&gt;
Participating algorithms will have to read audio in the following format:&lt;br /&gt;
&lt;br /&gt;
* Sample rate: 44.1 KHz&lt;br /&gt;
* Sample size: 16 bit&lt;br /&gt;
* Number of channels: 1 (mono)&lt;br /&gt;
* Encoding: WAV &lt;br /&gt;
&lt;br /&gt;
=== Output Data ===&lt;br /&gt;
&lt;br /&gt;
The downbeat estimation algorithms will return downbeat times in an ASCII text file for each input .wav audio file. The specification of this output file is immediately below.&lt;br /&gt;
&lt;br /&gt;
=== Output File Format (Audio Downbeat Estimation) ===&lt;br /&gt;
&lt;br /&gt;
The downbeat output file format is an ASCII text format. Each downbeat time is specified, in seconds, on its own line. Specifically, &lt;br /&gt;
&lt;br /&gt;
 &amp;lt;downbeat time (in seconds)&amp;gt;\n&lt;br /&gt;
&lt;br /&gt;
where \n denotes the end of line. The &amp;lt; and &amp;gt; characters are not included. An example output file would look something like:&lt;br /&gt;
&lt;br /&gt;
 0.243&lt;br /&gt;
 1.486&lt;br /&gt;
 2.729&lt;br /&gt;
&lt;br /&gt;
=== Algorithm Calling Format ===&lt;br /&gt;
&lt;br /&gt;
The submitted algorithm must take as arguments a SINGLE .wav file to perform the downbeat estimation as well as the full output path and filename of the output file. The ability to specify the output path and file name is essential. Denoting the input .wav file path and name as %input and the output file path and name as %output, a program called foobar could be called from the command-line as follows:&lt;br /&gt;
&lt;br /&gt;
 foobar %input %output&lt;br /&gt;
 foobar -i %input -o %output&lt;br /&gt;
&lt;br /&gt;
Moreover, if your submission takes additional parameters, such as a detection threshold, foobar could be called like:&lt;br /&gt;
&lt;br /&gt;
 foobar .1 %input %output&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output  &lt;br /&gt;
&lt;br /&gt;
If your submission is in MATLAB, it should be submitted as a function. Once again, the function must contain String inputs for the full path and names of the input and output files. Parameters could also be specified as input arguments of the function. For example: &lt;br /&gt;
&lt;br /&gt;
 foobar('%input','%output')&lt;br /&gt;
 foobar(.1,'%input','%output')&lt;br /&gt;
&lt;br /&gt;
=== README File ===&lt;br /&gt;
&lt;br /&gt;
A README file accompanying each submission should contain explicit instructions on how to to run the program (as well as contact information, etc.). In particular, each command line to run should be specified, using %input for the input sound file and %output for the resulting text file.&lt;br /&gt;
&lt;br /&gt;
For instance, to test the program foobar with different values for parameters param1, the README file would look like:&lt;br /&gt;
&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output&lt;br /&gt;
 foobar -param1 .15 -i %input -o %output&lt;br /&gt;
 foobar -param1 .2 -i %input -o %output&lt;br /&gt;
 foobar -param1 .25 -i %input -o %output&lt;br /&gt;
 foobar -param1 .3 -i %input -o %output&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
For a submission using MATLAB, the README file could look like:&lt;br /&gt;
&lt;br /&gt;
 matlab -r &amp;quot;foobar(.1,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.15,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.2,'%input','%output');quit;&amp;quot; &lt;br /&gt;
 matlab -r &amp;quot;foobar(.25,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.3,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
The different command lines to evaluate the performance of each parameter set over the whole database will be generated automatically from each line in the README file containing both '%input' and '%output' strings.&lt;br /&gt;
&lt;br /&gt;
== Evaluation Procedures ==&lt;br /&gt;
&lt;br /&gt;
For the evalution procedure we will use&lt;br /&gt;
*'''F-measure''' - the standard calculation as used in onset and beat tracking evaluation with a +/-70ms window, see Dixon (2007).&lt;br /&gt;
&lt;br /&gt;
== Time and hardware limits ==&lt;br /&gt;
Due to the potentially high number of participants in this and other audio tasks, hard limits on the runtime of submissions will be imposed.&lt;br /&gt;
&lt;br /&gt;
A hard limit of 24 hours will be imposed on analysis times. Submissions exceeding this limit may not receive a result.&lt;br /&gt;
&lt;br /&gt;
== Potential Participants ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
== Discussion ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
= Bibliography =&lt;br /&gt;
S. Dixon, F. Gouyon and G. Widmer, &amp;quot;Towards Characterisation of Music via Rhythmic Patterns&amp;quot;, In Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR 2004), pp 509-516.&lt;br /&gt;
&lt;br /&gt;
S. Dixon, &amp;quot;Evaluation of audio beat tracking system beatroot,&amp;quot; Journal of New Music Research, vol. 36, no. 1, pp. 39-51, 2007.&lt;br /&gt;
&lt;br /&gt;
J. A. Hockman, M. E. P. Davies, I. Fujinaga.&amp;quot;ONE IN THE JUNGLE: Downbeat Detection in Hardcore, Jungle, and Drum and Bass&amp;quot;, In Proceedings of 13th International Society for Music Information Retrieval Conference (ISMIR), Porto, Portugal pp. 169-174, 2012.&lt;br /&gt;
&lt;br /&gt;
F. Krebs, S. Boeck, and G. Widmer, &amp;quot;Rhythmic Pattern Modeling for Beat- and Downbeat Tracking in Musical Audio&amp;quot;, In Proceedings of 14th International Society for Music Information Retrieval Conference (ISMIR), Curitiba, Brazil, 2013.&lt;br /&gt;
&lt;br /&gt;
M. Mauch, C. Cannam, M. E. P. Davies, S. Dixon, C. Harte, S. Kolozali and D. Tidhar, &amp;quot;OMRAS2 Metadata Project 2009&amp;quot;, Late-breaking session at the 10th International Conference on Music Information Retrieval, 2009.&lt;br /&gt;
&lt;br /&gt;
A. Srinivasamurthy, A. Holzapfel, and Xavier Serra, &amp;quot;In Search of Automatic Rhythm Analysis Methods for Turkish and Indian Art Music&amp;quot;, Journal of New Music Research, vol. 43, no. 1, pp. 94-114, 2014.&lt;/div&gt;</summary>
		<author><name>Matthew Davies</name></author>
		
	</entry>
	<entry>
		<id>https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10059</id>
		<title>2014:Audio Downbeat Estimation</title>
		<link rel="alternate" type="text/html" href="https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10059"/>
		<updated>2014-05-26T14:23:34Z</updated>

		<summary type="html">&lt;p&gt;Matthew Davies: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Description ==&lt;br /&gt;
&lt;br /&gt;
'''This task is new for 2014!'''&lt;br /&gt;
&lt;br /&gt;
This text has been adapted from the Audio Beat Tracking Wiki page.  Please add your comments and discussion at the bottom of this page.&lt;br /&gt;
&lt;br /&gt;
The aim of the automatic downbeat estimation task is to identify the locations of downbeats in a collection of sound files. While this is similar to the Audio Beat Tracking task, here the aim is to find the first beat of each bar (measure) rather than all beat times. Algorithms are '''not''' required to estimate beat times or time-signature in addition to downbeats.&lt;br /&gt;
&lt;br /&gt;
Submitted algorithms will be evaluated in terms of their accuracy in finding downbeat locations (only) as annotated by musical experts across several diverse datasets.&lt;br /&gt;
&lt;br /&gt;
== Data ==&lt;br /&gt;
&lt;br /&gt;
=== Collections ===&lt;br /&gt;
'''Ballroom'''&lt;br /&gt;
The ballroom dataset contains eight different dance styles (Cha Cha, Jive, Quickstep, Rumba, Samba, Tango, Viennese Waltz and Waltz). It consists of 697 excerpts of 30s in duration.&lt;br /&gt;
The dataset contains two different meters 3/4 and 4/4 - but all pieces have constant meter. For further information see Dixon et al (2004) and Krebs et al (2013).&lt;br /&gt;
Note, we are using the ground truth annotations from Krebs et al. (2013) available at https://github.com/CPJKU/BallroomAnnotations&lt;br /&gt;
&lt;br /&gt;
'''Isophonics (Beatles only)'''&lt;br /&gt;
The Beatles dataset from the Centre for Digital Music at Queen Mary, University of London (http://www.isophonics.net/), as also used for Audio Chord Estimation in MIREX for many years. Available from http://www.isophonics.net/. &lt;br /&gt;
This dataset contains 179 complete songs (all except Revolution 9), the majority of which are in 4/4.&lt;br /&gt;
For further information see Mauch et al (2009).&lt;br /&gt;
&lt;br /&gt;
'''Turkish Data'''&lt;br /&gt;
The Turkish corpus is an extended version of the annotated data used in Srinivasamurthy et al. (2014). It includes 82 excerpts of one&lt;br /&gt;
minute length each, and each piece belongs to one of three&lt;br /&gt;
rhythm classes that are referred to as usul in Turkish Art&lt;br /&gt;
music. 32 pieces are in the 9/8-usul Aksak, 20 pieces&lt;br /&gt;
in the 10/8-usul Curcuna, and 30 samples in the 8/8-usul&lt;br /&gt;
Düyek.&lt;br /&gt;
&lt;br /&gt;
'''Cretan Data'''&lt;br /&gt;
The corpus of Cretan music consists of 42 full length pieces of Cretan leaping dances. While there are several dances that differ in terms of their steps, the differences in&lt;br /&gt;
the sound are most noticeable in the melodic content, and all pieces can be considered to belong to one rhythmic style. All these dances are usually notated using a 2/4 time signature,&lt;br /&gt;
and the accompanying rhythmical patterns are usually played on a Cretan lute. While a variety of rhythmic patterns exist, they do not relate to a specific dance and can be&lt;br /&gt;
assumed to occur in all of the 42 songs in this corpus.&lt;br /&gt;
&lt;br /&gt;
'''HJDB''' (to be confirmed)&lt;br /&gt;
236 excerpts of Hardcore, Jungle and Drum and Bass music between 30s and 2 minutes in length. All excerpts are in 4/4 and have a constant tempo. &lt;br /&gt;
For further information see Hockman et al (2012).&lt;br /&gt;
&lt;br /&gt;
In total this makes 1234 excerpts (of which 259 are full length songs).&lt;br /&gt;
&lt;br /&gt;
=== Audio Formats ===&lt;br /&gt;
&lt;br /&gt;
The data are monophonic sound files, with the associated onset times and data about the annotation robustness.&lt;br /&gt;
&lt;br /&gt;
* CD-quality (PCM, 16-bit, 44100 Hz) for all except Ballroom (originally lower quality, but resampled to 44100 Hz)&lt;br /&gt;
* single channel (mono)&lt;br /&gt;
&lt;br /&gt;
== Submission Format ==&lt;br /&gt;
Submissions to this task will have to conform to a specified format detailed below. Submissions should be packaged and contain at least two files: The algorithm itself and a README containing contact information and detailing, in full, the use of the algorithm.&lt;br /&gt;
&lt;br /&gt;
=== Input Data ===&lt;br /&gt;
Participating algorithms will have to read audio in the following format:&lt;br /&gt;
&lt;br /&gt;
* Sample rate: 44.1 KHz&lt;br /&gt;
* Sample size: 16 bit&lt;br /&gt;
* Number of channels: 1 (mono)&lt;br /&gt;
* Encoding: WAV &lt;br /&gt;
&lt;br /&gt;
=== Output Data ===&lt;br /&gt;
&lt;br /&gt;
The downbeat estimation algorithms will return downbeat times in an ASCII text file for each input .wav audio file. The specification of this output file is immediately below.&lt;br /&gt;
&lt;br /&gt;
=== Output File Format (Audio Downbeat Estimation) ===&lt;br /&gt;
&lt;br /&gt;
The downbeat output file format is an ASCII text format. Each downbeat time is specified, in seconds, on its own line. Specifically, &lt;br /&gt;
&lt;br /&gt;
 &amp;lt;downbeat time (in seconds)&amp;gt;\n&lt;br /&gt;
&lt;br /&gt;
where \n denotes the end of line. The &amp;lt; and &amp;gt; characters are not included. An example output file would look something like:&lt;br /&gt;
&lt;br /&gt;
 0.243&lt;br /&gt;
 1.486&lt;br /&gt;
 2.729&lt;br /&gt;
&lt;br /&gt;
=== Algorithm Calling Format ===&lt;br /&gt;
&lt;br /&gt;
The submitted algorithm must take as arguments a SINGLE .wav file to perform the downbeat estimation as well as the full output path and filename of the output file. The ability to specify the output path and file name is essential. Denoting the input .wav file path and name as %input and the output file path and name as %output, a program called foobar could be called from the command-line as follows:&lt;br /&gt;
&lt;br /&gt;
 foobar %input %output&lt;br /&gt;
 foobar -i %input -o %output&lt;br /&gt;
&lt;br /&gt;
Moreover, if your submission takes additional parameters, such as a detection threshold, foobar could be called like:&lt;br /&gt;
&lt;br /&gt;
 foobar .1 %input %output&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output  &lt;br /&gt;
&lt;br /&gt;
If your submission is in MATLAB, it should be submitted as a function. Once again, the function must contain String inputs for the full path and names of the input and output files. Parameters could also be specified as input arguments of the function. For example: &lt;br /&gt;
&lt;br /&gt;
 foobar('%input','%output')&lt;br /&gt;
 foobar(.1,'%input','%output')&lt;br /&gt;
&lt;br /&gt;
=== README File ===&lt;br /&gt;
&lt;br /&gt;
A README file accompanying each submission should contain explicit instructions on how to to run the program (as well as contact information, etc.). In particular, each command line to run should be specified, using %input for the input sound file and %output for the resulting text file.&lt;br /&gt;
&lt;br /&gt;
For instance, to test the program foobar with different values for parameters param1, the README file would look like:&lt;br /&gt;
&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output&lt;br /&gt;
 foobar -param1 .15 -i %input -o %output&lt;br /&gt;
 foobar -param1 .2 -i %input -o %output&lt;br /&gt;
 foobar -param1 .25 -i %input -o %output&lt;br /&gt;
 foobar -param1 .3 -i %input -o %output&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
For a submission using MATLAB, the README file could look like:&lt;br /&gt;
&lt;br /&gt;
 matlab -r &amp;quot;foobar(.1,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.15,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.2,'%input','%output');quit;&amp;quot; &lt;br /&gt;
 matlab -r &amp;quot;foobar(.25,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.3,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
The different command lines to evaluate the performance of each parameter set over the whole database will be generated automatically from each line in the README file containing both '%input' and '%output' strings.&lt;br /&gt;
&lt;br /&gt;
== Evaluation Procedures ==&lt;br /&gt;
&lt;br /&gt;
For the evalution procedure we will use&lt;br /&gt;
*'''F-measure''' - the standard calculation as used in onset and beat tracking evaluation with a +/-70ms window, see Dixon (2007).&lt;br /&gt;
&lt;br /&gt;
== Time and hardware limits ==&lt;br /&gt;
Due to the potentially high number of participants in this and other audio tasks, hard limits on the runtime of submissions will be imposed.&lt;br /&gt;
&lt;br /&gt;
A hard limit of 24 hours will be imposed on analysis times. Submissions exceeding this limit may not receive a result.&lt;br /&gt;
&lt;br /&gt;
== Potential Participants ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
== Discussion ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
= Bibliography =&lt;br /&gt;
S. Dixon, F. Gouyon and G. Widmer, &amp;quot;Towards Characterisation of Music via Rhythmic Patterns&amp;quot;, In Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR 2004), pp 509-516.&lt;br /&gt;
&lt;br /&gt;
S. Dixon, &amp;quot;Evaluation of audio beat tracking system beatroot,&amp;quot; Journal of New Music Research, vol. 36, no. 1, pp. 39-51, 2007.&lt;br /&gt;
&lt;br /&gt;
J. A. Hockman, M. E. P. Davies, I. Fujinaga.&amp;quot;ONE IN THE JUNGLE: Downbeat Detection in Hardcore, Jungle, and Drum and Bass&amp;quot;, In Proceedings of 13th International Society for Music Information Retrieval Conference (ISMIR), Porto, Portugal pp. 169-174, 2012.&lt;br /&gt;
&lt;br /&gt;
F. Krebs, S. Boeck, and G. Widmer, &amp;quot;Rhythmic Pattern Modeling for Beat- and Downbeat Tracking in Musical Audio&amp;quot;, In Proceedings of 14th International Society for Music Information Retrieval Conference (ISMIR), Curitiba, Brazil, 2013.&lt;br /&gt;
&lt;br /&gt;
M. Mauch, C. Cannam, M. E. P. Davies, S. Dixon, C. Harte, S. Kolozali and D. Tidhar, &amp;quot;OMRAS2 Metadata Project 2009&amp;quot;, Late-breaking session at the 10th International Conference on Music Information Retrieval, 2009.&lt;br /&gt;
&lt;br /&gt;
A. Srinivasamurthy, A. Holzapfel, and Xavier Serra, &amp;quot;In Search of Automatic Rhythm Analysis Methods for Turkish and Indian Art Music&amp;quot;, Journal of New Music Research, vol. 43, no. 1, pp. 94-114, 2014.&lt;/div&gt;</summary>
		<author><name>Matthew Davies</name></author>
		
	</entry>
	<entry>
		<id>https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10052</id>
		<title>2014:Audio Downbeat Estimation</title>
		<link rel="alternate" type="text/html" href="https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10052"/>
		<updated>2014-05-23T15:33:20Z</updated>

		<summary type="html">&lt;p&gt;Matthew Davies: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Description ==&lt;br /&gt;
&lt;br /&gt;
'''This task is new for 2014!'''&lt;br /&gt;
&lt;br /&gt;
This text has been adapted from the Audio Beat Tracking Wiki page.  Please add your comments and discussion at the bottom of this page.&lt;br /&gt;
&lt;br /&gt;
The aim of the automatic downbeat estimation task is to identify the locations of downbeats in a collection of sound files. While this is similar to the Audio Beat Tracking task, here the aim is to find the first beat of each bar (measure) rather than all beat times. Algorithms are '''not''' required to estimate beat times or time-signature in addition to downbeats.&lt;br /&gt;
&lt;br /&gt;
Submitted algorithms will be evaluated in terms of their accuracy in finding downbeat locations (only) as annotated by musical experts across several diverse datasets.&lt;br /&gt;
&lt;br /&gt;
== Data ==&lt;br /&gt;
&lt;br /&gt;
=== Collections ===&lt;br /&gt;
'''Ballroom'''&lt;br /&gt;
The ballroom dataset contains eight different dance styles (Cha Cha, Jive, Quickstep, Rumba, Samba, Tango, Viennese Waltz and Waltz). It consists of 697 excerpts of 30s in duration.&lt;br /&gt;
The dataset contains two different meters 3/4 and 4/4 - but all pieces have constant meter. For further information see Dixon et al (2004) and Krebs et al (2013).&lt;br /&gt;
Note, we are using the ground truth annotations from Krebs et al. (2013) available at https://github.com/CPJKU/BallroomAnnotations&lt;br /&gt;
&lt;br /&gt;
'''Isophonics (Beatles only)'''&lt;br /&gt;
The Beatles dataset from the Centre for Digital Music at Queen Mary, University of London (http://www.isophonics.net/), as also used for Audio Chord Estimation in MIREX for many years. Available from http://www.isophonics.net/. &lt;br /&gt;
This dataset contains 179 complete songs, the majority of which are in 4/4.&lt;br /&gt;
For further information see Mauch et al (2009).&lt;br /&gt;
&lt;br /&gt;
'''Turkish Data'''&lt;br /&gt;
The Turkish corpus is an extended version of the annotated data used in Srinivasamurthy et al. (2014). It includes 82 excerpts of one&lt;br /&gt;
minute length each, and each piece belongs to one of three&lt;br /&gt;
rhythm classes that are referred to as usul in Turkish Art&lt;br /&gt;
music. 32 pieces are in the 9/8-usul Aksak, 20 pieces&lt;br /&gt;
in the 10/8-usul Curcuna, and 30 samples in the 8/8-usul&lt;br /&gt;
Düyek.&lt;br /&gt;
&lt;br /&gt;
'''Cretan Data'''&lt;br /&gt;
The corpus of Cretan music consists of 42 full length pieces of Cretan leaping dances. While there are several dances that differ in terms of their steps, the differences in&lt;br /&gt;
the sound are most noticeable in the melodic content, and all pieces can be considered to belong to one rhythmic style. All these dances are usually notated using a 2/4 time signature,&lt;br /&gt;
and the accompanying rhythmical patterns are usually played on a Cretan lute. While a variety of rhythmic patterns exist, they do not relate to a specific dance and can be&lt;br /&gt;
assumed to occur in all of the 42 songs in this corpus.&lt;br /&gt;
&lt;br /&gt;
'''HJDB''' (to be confirmed)&lt;br /&gt;
236 excerpts of Hardcore, Jungle and Drum and Bass music between 30s and 2 minutes in length. All excerpts are in 4/4 and have a constant tempo. &lt;br /&gt;
For further information see Hockman et al (2012).&lt;br /&gt;
&lt;br /&gt;
In total this makes 1234 excerpts (of which 259 are full length songs).&lt;br /&gt;
&lt;br /&gt;
=== Audio Formats ===&lt;br /&gt;
&lt;br /&gt;
The data are monophonic sound files, with the associated onset times and data about the annotation robustness.&lt;br /&gt;
&lt;br /&gt;
* CD-quality (PCM, 16-bit, 44100 Hz) for all except Ballroom (originally lower quality, but resampled to 44100 Hz)&lt;br /&gt;
* single channel (mono)&lt;br /&gt;
&lt;br /&gt;
== Submission Format ==&lt;br /&gt;
Submissions to this task will have to conform to a specified format detailed below. Submissions should be packaged and contain at least two files: The algorithm itself and a README containing contact information and detailing, in full, the use of the algorithm.&lt;br /&gt;
&lt;br /&gt;
=== Input Data ===&lt;br /&gt;
Participating algorithms will have to read audio in the following format:&lt;br /&gt;
&lt;br /&gt;
* Sample rate: 44.1 KHz&lt;br /&gt;
* Sample size: 16 bit&lt;br /&gt;
* Number of channels: 1 (mono)&lt;br /&gt;
* Encoding: WAV &lt;br /&gt;
&lt;br /&gt;
=== Output Data ===&lt;br /&gt;
&lt;br /&gt;
The downbeat estimation algorithms will return downbeat times in an ASCII text file for each input .wav audio file. The specification of this output file is immediately below.&lt;br /&gt;
&lt;br /&gt;
=== Output File Format (Audio Downbeat Estimation) ===&lt;br /&gt;
&lt;br /&gt;
The downbeat output file format is an ASCII text format. Each downbeat time is specified, in seconds, on its own line. Specifically, &lt;br /&gt;
&lt;br /&gt;
 &amp;lt;downbeat time (in seconds)&amp;gt;\n&lt;br /&gt;
&lt;br /&gt;
where \n denotes the end of line. The &amp;lt; and &amp;gt; characters are not included. An example output file would look something like:&lt;br /&gt;
&lt;br /&gt;
 0.243&lt;br /&gt;
 1.486&lt;br /&gt;
 2.729&lt;br /&gt;
&lt;br /&gt;
=== Algorithm Calling Format ===&lt;br /&gt;
&lt;br /&gt;
The submitted algorithm must take as arguments a SINGLE .wav file to perform the downbeat estimation as well as the full output path and filename of the output file. The ability to specify the output path and file name is essential. Denoting the input .wav file path and name as %input and the output file path and name as %output, a program called foobar could be called from the command-line as follows:&lt;br /&gt;
&lt;br /&gt;
 foobar %input %output&lt;br /&gt;
 foobar -i %input -o %output&lt;br /&gt;
&lt;br /&gt;
Moreover, if your submission takes additional parameters, such as a detection threshold, foobar could be called like:&lt;br /&gt;
&lt;br /&gt;
 foobar .1 %input %output&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output  &lt;br /&gt;
&lt;br /&gt;
If your submission is in MATLAB, it should be submitted as a function. Once again, the function must contain String inputs for the full path and names of the input and output files. Parameters could also be specified as input arguments of the function. For example: &lt;br /&gt;
&lt;br /&gt;
 foobar('%input','%output')&lt;br /&gt;
 foobar(.1,'%input','%output')&lt;br /&gt;
&lt;br /&gt;
=== README File ===&lt;br /&gt;
&lt;br /&gt;
A README file accompanying each submission should contain explicit instructions on how to to run the program (as well as contact information, etc.). In particular, each command line to run should be specified, using %input for the input sound file and %output for the resulting text file.&lt;br /&gt;
&lt;br /&gt;
For instance, to test the program foobar with different values for parameters param1, the README file would look like:&lt;br /&gt;
&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output&lt;br /&gt;
 foobar -param1 .15 -i %input -o %output&lt;br /&gt;
 foobar -param1 .2 -i %input -o %output&lt;br /&gt;
 foobar -param1 .25 -i %input -o %output&lt;br /&gt;
 foobar -param1 .3 -i %input -o %output&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
For a submission using MATLAB, the README file could look like:&lt;br /&gt;
&lt;br /&gt;
 matlab -r &amp;quot;foobar(.1,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.15,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.2,'%input','%output');quit;&amp;quot; &lt;br /&gt;
 matlab -r &amp;quot;foobar(.25,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.3,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
The different command lines to evaluate the performance of each parameter set over the whole database will be generated automatically from each line in the README file containing both '%input' and '%output' strings.&lt;br /&gt;
&lt;br /&gt;
== Evaluation Procedures ==&lt;br /&gt;
&lt;br /&gt;
For the evalution procedure we will use&lt;br /&gt;
*'''F-measure''' - the standard calculation as used in onset and beat tracking evaluation with a +/-70ms window, see Dixon (2007).&lt;br /&gt;
&lt;br /&gt;
== Time and hardware limits ==&lt;br /&gt;
Due to the potentially high number of participants in this and other audio tasks, hard limits on the runtime of submissions will be imposed.&lt;br /&gt;
&lt;br /&gt;
A hard limit of 24 hours will be imposed on analysis times. Submissions exceeding this limit may not receive a result.&lt;br /&gt;
&lt;br /&gt;
== Potential Participants ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
== Discussion ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
= Bibliography =&lt;br /&gt;
S. Dixon, F. Gouyon and G. Widmer, &amp;quot;Towards Characterisation of Music via Rhythmic Patterns&amp;quot;, In Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR 2004), pp 509-516.&lt;br /&gt;
&lt;br /&gt;
S. Dixon, &amp;quot;Evaluation of audio beat tracking system beatroot,&amp;quot; Journal of New Music Research, vol. 36, no. 1, pp. 39-51, 2007.&lt;br /&gt;
&lt;br /&gt;
J. A. Hockman, M. E. P. Davies, I. Fujinaga.&amp;quot;ONE IN THE JUNGLE: Downbeat Detection in Hardcore, Jungle, and Drum and Bass&amp;quot;, In Proceedings of 13th International Society for Music Information Retrieval Conference (ISMIR), Porto, Portugal pp. 169-174, 2012.&lt;br /&gt;
&lt;br /&gt;
F. Krebs, S. Boeck, and G. Widmer, &amp;quot;Rhythmic Pattern Modeling for Beat- and Downbeat Tracking in Musical Audio&amp;quot;, In Proceedings of 14th International Society for Music Information Retrieval Conference (ISMIR), Curitiba, Brazil, 2013.&lt;br /&gt;
&lt;br /&gt;
M. Mauch, C. Cannam, M. E. P. Davies, S. Dixon, C. Harte, S. Kolozali and D. Tidhar, &amp;quot;OMRAS2 Metadata Project 2009&amp;quot;, Late-breaking session at the 10th International Conference on Music Information Retrieval, 2009.&lt;br /&gt;
&lt;br /&gt;
A. Srinivasamurthy, A. Holzapfel, and Xavier Serra, &amp;quot;In Search of Automatic Rhythm Analysis Methods for Turkish and Indian Art Music&amp;quot;, Journal of New Music Research, vol. 43, no. 1, pp. 94-114, 2014.&lt;/div&gt;</summary>
		<author><name>Matthew Davies</name></author>
		
	</entry>
	<entry>
		<id>https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10051</id>
		<title>2014:Audio Downbeat Estimation</title>
		<link rel="alternate" type="text/html" href="https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10051"/>
		<updated>2014-05-23T15:15:18Z</updated>

		<summary type="html">&lt;p&gt;Matthew Davies: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Description ==&lt;br /&gt;
&lt;br /&gt;
'''This task is new for 2014!'''&lt;br /&gt;
&lt;br /&gt;
This text has been adapted from the Audio Beat Tracking Wiki page.  Please add your comments and discussion at the bottom of this page.&lt;br /&gt;
&lt;br /&gt;
The aim of the automatic downbeat estimation task is to identify the locations of downbeats in a collection of sound files. While this is similar to the Audio Beat Tracking task, here the aim is to find the first beat of each bar (measure) rather than all beat times. Algorithms are '''not''' required to estimate beat times or time-signature in addition to downbeats.&lt;br /&gt;
&lt;br /&gt;
Submitted algorithms will be evaluated in terms of their accuracy in finding downbeat locations (only) as annotated by musical experts across several diverse datasets.&lt;br /&gt;
&lt;br /&gt;
== Data ==&lt;br /&gt;
&lt;br /&gt;
=== Collections ===&lt;br /&gt;
'''Ballroom'''&lt;br /&gt;
The ballroom dataset contains eight different dance styles (Cha Cha, Jive, Rumba, Samba, Tango, Viennese Waltz and Waltz). It consists of 697 excerpts of 30s in duration.&lt;br /&gt;
The dataset contains two different meters 3/4 and 4/4 - but all pieces have constant meter. For further information see Dixon et al (2004) and Krebs et al (2013).&lt;br /&gt;
Note, we are using the ground truth annotations from Krebs et al. (2013) available at https://github.com/CPJKU/BallroomAnnotations&lt;br /&gt;
&lt;br /&gt;
'''Isophonics (Beatles only)'''&lt;br /&gt;
The Beatles dataset from the Centre for Digital Music at Queen Mary, University of London (http://www.isophonics.net/), as also used for Audio Chord Estimation in MIREX for many years. Available from http://www.isophonics.net/. &lt;br /&gt;
This dataset contains 179 complete songs, the majority of which are in 4/4.&lt;br /&gt;
For further information see Mauch et al (2009).&lt;br /&gt;
&lt;br /&gt;
'''Turkish Data'''&lt;br /&gt;
The Turkish corpus is an extended version of the annotated data used in Srinivasamurthy et al. (2014). It includes 82 excerpts of one&lt;br /&gt;
minute length each, and each piece belongs to one of three&lt;br /&gt;
rhythm classes that are referred to as usul in Turkish Art&lt;br /&gt;
music. 32 pieces are in the 9/8-usul Aksak, 20 pieces&lt;br /&gt;
in the 10/8-usul Curcuna, and 30 samples in the 8/8-usul&lt;br /&gt;
Düyek.&lt;br /&gt;
&lt;br /&gt;
'''Cretan Data'''&lt;br /&gt;
The corpus of Cretan music consists of 42 full length pieces of Cretan leaping dances. While there are several dances that differ in terms of their steps, the differences in&lt;br /&gt;
the sound are most noticeable in the melodic content, and all pieces can be considered to belong to one rhythmic style. All these dances are usually notated using a 2/4 time signature,&lt;br /&gt;
and the accompanying rhythmical patterns are usually played on a Cretan lute. While a variety of rhythmic patterns exist, they do not relate to a specific dance and can be&lt;br /&gt;
assumed to occur in all of the 42 songs in this corpus.&lt;br /&gt;
&lt;br /&gt;
'''HJDB''' (to be confirmed)&lt;br /&gt;
236 excerpts of Hardcore, Jungle and Drum and Bass music between 30s and 2 minutes in length. All excerpts are in 4/4 and have a constant tempo. &lt;br /&gt;
For further information see Hockman et al (2012).&lt;br /&gt;
&lt;br /&gt;
In total this makes 1234 excerpts (of which 259 are full length songs).&lt;br /&gt;
&lt;br /&gt;
=== Audio Formats ===&lt;br /&gt;
&lt;br /&gt;
The data are monophonic sound files, with the associated onset times and data about the annotation robustness.&lt;br /&gt;
&lt;br /&gt;
* CD-quality (PCM, 16-bit, 44100 Hz) for all except Ballroom (originally lower quality, but resampled to 44100 Hz)&lt;br /&gt;
* single channel (mono)&lt;br /&gt;
&lt;br /&gt;
== Submission Format ==&lt;br /&gt;
Submissions to this task will have to conform to a specified format detailed below. Submissions should be packaged and contain at least two files: The algorithm itself and a README containing contact information and detailing, in full, the use of the algorithm.&lt;br /&gt;
&lt;br /&gt;
=== Input Data ===&lt;br /&gt;
Participating algorithms will have to read audio in the following format:&lt;br /&gt;
&lt;br /&gt;
* Sample rate: 44.1 KHz&lt;br /&gt;
* Sample size: 16 bit&lt;br /&gt;
* Number of channels: 1 (mono)&lt;br /&gt;
* Encoding: WAV &lt;br /&gt;
&lt;br /&gt;
=== Output Data ===&lt;br /&gt;
&lt;br /&gt;
The downbeat estimation algorithms will return downbeat times in an ASCII text file for each input .wav audio file. The specification of this output file is immediately below.&lt;br /&gt;
&lt;br /&gt;
=== Output File Format (Audio Downbeat Estimation) ===&lt;br /&gt;
&lt;br /&gt;
The downbeat output file format is an ASCII text format. Each downbeat time is specified, in seconds, on its own line. Specifically, &lt;br /&gt;
&lt;br /&gt;
 &amp;lt;downbeat time (in seconds)&amp;gt;\n&lt;br /&gt;
&lt;br /&gt;
where \n denotes the end of line. The &amp;lt; and &amp;gt; characters are not included. An example output file would look something like:&lt;br /&gt;
&lt;br /&gt;
 0.243&lt;br /&gt;
 1.486&lt;br /&gt;
 2.729&lt;br /&gt;
&lt;br /&gt;
=== Algorithm Calling Format ===&lt;br /&gt;
&lt;br /&gt;
The submitted algorithm must take as arguments a SINGLE .wav file to perform the downbeat estimation as well as the full output path and filename of the output file. The ability to specify the output path and file name is essential. Denoting the input .wav file path and name as %input and the output file path and name as %output, a program called foobar could be called from the command-line as follows:&lt;br /&gt;
&lt;br /&gt;
 foobar %input %output&lt;br /&gt;
 foobar -i %input -o %output&lt;br /&gt;
&lt;br /&gt;
Moreover, if your submission takes additional parameters, such as a detection threshold, foobar could be called like:&lt;br /&gt;
&lt;br /&gt;
 foobar .1 %input %output&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output  &lt;br /&gt;
&lt;br /&gt;
If your submission is in MATLAB, it should be submitted as a function. Once again, the function must contain String inputs for the full path and names of the input and output files. Parameters could also be specified as input arguments of the function. For example: &lt;br /&gt;
&lt;br /&gt;
 foobar('%input','%output')&lt;br /&gt;
 foobar(.1,'%input','%output')&lt;br /&gt;
&lt;br /&gt;
=== README File ===&lt;br /&gt;
&lt;br /&gt;
A README file accompanying each submission should contain explicit instructions on how to to run the program (as well as contact information, etc.). In particular, each command line to run should be specified, using %input for the input sound file and %output for the resulting text file.&lt;br /&gt;
&lt;br /&gt;
For instance, to test the program foobar with different values for parameters param1, the README file would look like:&lt;br /&gt;
&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output&lt;br /&gt;
 foobar -param1 .15 -i %input -o %output&lt;br /&gt;
 foobar -param1 .2 -i %input -o %output&lt;br /&gt;
 foobar -param1 .25 -i %input -o %output&lt;br /&gt;
 foobar -param1 .3 -i %input -o %output&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
For a submission using MATLAB, the README file could look like:&lt;br /&gt;
&lt;br /&gt;
 matlab -r &amp;quot;foobar(.1,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.15,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.2,'%input','%output');quit;&amp;quot; &lt;br /&gt;
 matlab -r &amp;quot;foobar(.25,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.3,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
The different command lines to evaluate the performance of each parameter set over the whole database will be generated automatically from each line in the README file containing both '%input' and '%output' strings.&lt;br /&gt;
&lt;br /&gt;
== Evaluation Procedures ==&lt;br /&gt;
&lt;br /&gt;
For the evalution procedure we will use&lt;br /&gt;
*'''F-measure''' - the standard calculation as used in onset and beat tracking evaluation with a +/-70ms window, see Dixon (2007).&lt;br /&gt;
&lt;br /&gt;
== Time and hardware limits ==&lt;br /&gt;
Due to the potentially high number of participants in this and other audio tasks, hard limits on the runtime of submissions will be imposed.&lt;br /&gt;
&lt;br /&gt;
A hard limit of 24 hours will be imposed on analysis times. Submissions exceeding this limit may not receive a result.&lt;br /&gt;
&lt;br /&gt;
== Potential Participants ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
== Discussion ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
= Bibliography =&lt;br /&gt;
S. Dixon, F. Gouyon and G. Widmer, &amp;quot;Towards Characterisation of Music via Rhythmic Patterns&amp;quot;, In Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR 2004), pp 509-516.&lt;br /&gt;
&lt;br /&gt;
S. Dixon, &amp;quot;Evaluation of audio beat tracking system beatroot,&amp;quot; Journal of New Music Research, vol. 36, no. 1, pp. 39-51, 2007.&lt;br /&gt;
&lt;br /&gt;
J. A. Hockman, M. E. P. Davies, I. Fujinaga.&amp;quot;ONE IN THE JUNGLE: Downbeat Detection in Hardcore, Jungle, and Drum and Bass&amp;quot;, In Proceedings of 13th International Society for Music Information Retrieval Conference (ISMIR), Porto, Portugal pp. 169-174, 2012.&lt;br /&gt;
&lt;br /&gt;
F. Krebs, S. Boeck, and G. Widmer, &amp;quot;Rhythmic Pattern Modeling for Beat- and Downbeat Tracking in Musical Audio&amp;quot;, In Proceedings of 14th International Society for Music Information Retrieval Conference (ISMIR), Curitiba, Brazil, 2013.&lt;br /&gt;
&lt;br /&gt;
M. Mauch, C. Cannam, M. E. P. Davies, S. Dixon, C. Harte, S. Kolozali and D. Tidhar, &amp;quot;OMRAS2 Metadata Project 2009&amp;quot;, Late-breaking session at the 10th International Conference on Music Information Retrieval, 2009.&lt;br /&gt;
&lt;br /&gt;
A. Srinivasamurthy, A. Holzapfel, and Xavier Serra, &amp;quot;In Search of Automatic Rhythm Analysis Methods for Turkish and Indian Art Music&amp;quot;, Journal of New Music Research, vol. 43, no. 1, pp. 94-114, 2014.&lt;/div&gt;</summary>
		<author><name>Matthew Davies</name></author>
		
	</entry>
	<entry>
		<id>https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10050</id>
		<title>2014:Audio Downbeat Estimation</title>
		<link rel="alternate" type="text/html" href="https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10050"/>
		<updated>2014-05-23T15:11:17Z</updated>

		<summary type="html">&lt;p&gt;Matthew Davies: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Description ==&lt;br /&gt;
&lt;br /&gt;
'''This task is new for 2014!'''&lt;br /&gt;
&lt;br /&gt;
This text has been adapted from the Audio Beat Tracking Wiki page.  Please add your comments and discussion at the bottom of this page.&lt;br /&gt;
&lt;br /&gt;
The aim of the automatic downbeat estimation task is to identify the locations of downbeats in a collection of sound files. While this is similar to the Audio Beat Tracking task, here the aim is to find the first beat of each bar (measure) rather than all beat times. Algorithms are '''not''' required to estimate beat times in addition to downbeats.&lt;br /&gt;
&lt;br /&gt;
Submitted algorithms will be evaluated in terms of their accuracy in finding downbeat locations (only) as annotated by musical experts across several diverse datasets.&lt;br /&gt;
&lt;br /&gt;
== Data ==&lt;br /&gt;
&lt;br /&gt;
=== Collections ===&lt;br /&gt;
'''Ballroom'''&lt;br /&gt;
The ballroom dataset contains eight different dance styles (Cha Cha, Jive, Rumba, Samba, Tango, Viennese Waltz and Waltz). It consists of 697 excerpts of 30s in duration.&lt;br /&gt;
The dataset contains two different meters 3/4 and 4/4 - but all pieces have constant meter. For further information see Dixon et al (2004) and Krebs et al (2013).&lt;br /&gt;
Note, we are using the ground truth annotations from Krebs et al. (2013) available at https://github.com/CPJKU/BallroomAnnotations&lt;br /&gt;
&lt;br /&gt;
'''Isophonics (Beatles only)'''&lt;br /&gt;
The Beatles dataset from the Centre for Digital Music at Queen Mary, University of London (http://www.isophonics.net/), as also used for Audio Chord Estimation in MIREX for many years. Available from http://www.isophonics.net/. &lt;br /&gt;
This dataset contains 179 complete songs, the majority of which are in 4/4.&lt;br /&gt;
For further information see Mauch et al (2009).&lt;br /&gt;
&lt;br /&gt;
'''Turkish Data'''&lt;br /&gt;
The Turkish corpus is an extended version of the annotated data used in Srinivasamurthy et al. (2014). It includes 82 excerpts of one&lt;br /&gt;
minute length each, and each piece belongs to one of three&lt;br /&gt;
rhythm classes that are referred to as usul in Turkish Art&lt;br /&gt;
music. 32 pieces are in the 9/8-usul Aksak, 20 pieces&lt;br /&gt;
in the 10/8-usul Curcuna, and 30 samples in the 8/8-usul&lt;br /&gt;
Düyek.&lt;br /&gt;
&lt;br /&gt;
'''Cretan Data'''&lt;br /&gt;
The corpus of Cretan music consists of 42 full length pieces of Cretan leaping dances. While there are several dances that differ in terms of their steps, the differences in&lt;br /&gt;
the sound are most noticeable in the melodic content, and all pieces can be considered to belong to one rhythmic style. All these dances are usually notated using a 2/4 time signature,&lt;br /&gt;
and the accompanying rhythmical patterns are usually played on a Cretan lute. While a variety of rhythmic patterns exist, they do not relate to a specific dance and can be&lt;br /&gt;
assumed to occur in all of the 42 songs in this corpus.&lt;br /&gt;
&lt;br /&gt;
'''HJDB''' (to be confirmed)&lt;br /&gt;
236 excerpts of Hardcore, Jungle and Drum and Bass music between 30s and 2 minutes in length. All excerpts are in 4/4 and have a constant tempo. &lt;br /&gt;
For further information see Hockman et al (2012).&lt;br /&gt;
&lt;br /&gt;
In total this makes 1234 excerpts (of which 259 are full length songs).&lt;br /&gt;
&lt;br /&gt;
=== Audio Formats ===&lt;br /&gt;
&lt;br /&gt;
The data are monophonic sound files, with the associated onset times and data about the annotation robustness.&lt;br /&gt;
&lt;br /&gt;
* CD-quality (PCM, 16-bit, 44100 Hz) for all except Ballroom (originally lower quality, but resampled to 44100 Hz)&lt;br /&gt;
* single channel (mono)&lt;br /&gt;
&lt;br /&gt;
== Submission Format ==&lt;br /&gt;
Submissions to this task will have to conform to a specified format detailed below. Submissions should be packaged and contain at least two files: The algorithm itself and a README containing contact information and detailing, in full, the use of the algorithm.&lt;br /&gt;
&lt;br /&gt;
=== Input Data ===&lt;br /&gt;
Participating algorithms will have to read audio in the following format:&lt;br /&gt;
&lt;br /&gt;
* Sample rate: 44.1 KHz&lt;br /&gt;
* Sample size: 16 bit&lt;br /&gt;
* Number of channels: 1 (mono)&lt;br /&gt;
* Encoding: WAV &lt;br /&gt;
&lt;br /&gt;
=== Output Data ===&lt;br /&gt;
&lt;br /&gt;
The downbeat estimation algorithms will return downbeat times in an ASCII text file for each input .wav audio file. The specification of this output file is immediately below.&lt;br /&gt;
&lt;br /&gt;
=== Output File Format (Audio Downbeat Estimation) ===&lt;br /&gt;
&lt;br /&gt;
The downbeat output file format is an ASCII text format. Each downbeat time is specified, in seconds, on its own line. Specifically, &lt;br /&gt;
&lt;br /&gt;
 &amp;lt;downbeat time (in seconds)&amp;gt;\n&lt;br /&gt;
&lt;br /&gt;
where \n denotes the end of line. The &amp;lt; and &amp;gt; characters are not included. An example output file would look something like:&lt;br /&gt;
&lt;br /&gt;
 0.243&lt;br /&gt;
 1.486&lt;br /&gt;
 2.729&lt;br /&gt;
&lt;br /&gt;
=== Algorithm Calling Format ===&lt;br /&gt;
&lt;br /&gt;
The submitted algorithm must take as arguments a SINGLE .wav file to perform the downbeat estimation as well as the full output path and filename of the output file. The ability to specify the output path and file name is essential. Denoting the input .wav file path and name as %input and the output file path and name as %output, a program called foobar could be called from the command-line as follows:&lt;br /&gt;
&lt;br /&gt;
 foobar %input %output&lt;br /&gt;
 foobar -i %input -o %output&lt;br /&gt;
&lt;br /&gt;
Moreover, if your submission takes additional parameters, such as a detection threshold, foobar could be called like:&lt;br /&gt;
&lt;br /&gt;
 foobar .1 %input %output&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output  &lt;br /&gt;
&lt;br /&gt;
If your submission is in MATLAB, it should be submitted as a function. Once again, the function must contain String inputs for the full path and names of the input and output files. Parameters could also be specified as input arguments of the function. For example: &lt;br /&gt;
&lt;br /&gt;
 foobar('%input','%output')&lt;br /&gt;
 foobar(.1,'%input','%output')&lt;br /&gt;
&lt;br /&gt;
=== README File ===&lt;br /&gt;
&lt;br /&gt;
A README file accompanying each submission should contain explicit instructions on how to to run the program (as well as contact information, etc.). In particular, each command line to run should be specified, using %input for the input sound file and %output for the resulting text file.&lt;br /&gt;
&lt;br /&gt;
For instance, to test the program foobar with different values for parameters param1, the README file would look like:&lt;br /&gt;
&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output&lt;br /&gt;
 foobar -param1 .15 -i %input -o %output&lt;br /&gt;
 foobar -param1 .2 -i %input -o %output&lt;br /&gt;
 foobar -param1 .25 -i %input -o %output&lt;br /&gt;
 foobar -param1 .3 -i %input -o %output&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
For a submission using MATLAB, the README file could look like:&lt;br /&gt;
&lt;br /&gt;
 matlab -r &amp;quot;foobar(.1,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.15,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.2,'%input','%output');quit;&amp;quot; &lt;br /&gt;
 matlab -r &amp;quot;foobar(.25,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.3,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
The different command lines to evaluate the performance of each parameter set over the whole database will be generated automatically from each line in the README file containing both '%input' and '%output' strings.&lt;br /&gt;
&lt;br /&gt;
== Evaluation Procedures ==&lt;br /&gt;
&lt;br /&gt;
For the evalution procedure we will use&lt;br /&gt;
*'''F-measure''' - the standard calculation as used in onset and beat tracking evaluation with a +/-70ms window, see Dixon (2007).&lt;br /&gt;
&lt;br /&gt;
== Time and hardware limits ==&lt;br /&gt;
Due to the potentially high number of participants in this and other audio tasks, hard limits on the runtime of submissions will be imposed.&lt;br /&gt;
&lt;br /&gt;
A hard limit of 24 hours will be imposed on analysis times. Submissions exceeding this limit may not receive a result.&lt;br /&gt;
&lt;br /&gt;
== Potential Participants ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
== Discussion ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
= Bibliography =&lt;br /&gt;
S. Dixon, F. Gouyon and G. Widmer, &amp;quot;Towards Characterisation of Music via Rhythmic Patterns&amp;quot;, In Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR 2004), pp 509-516.&lt;br /&gt;
&lt;br /&gt;
S. Dixon, &amp;quot;Evaluation of audio beat tracking system beatroot,&amp;quot; Journal of New Music Research, vol. 36, no. 1, pp. 39-51, 2007.&lt;br /&gt;
&lt;br /&gt;
J. A. Hockman, M. E. P. Davies, I. Fujinaga.&amp;quot;ONE IN THE JUNGLE: Downbeat Detection in Hardcore, Jungle, and Drum and Bass&amp;quot;, In Proceedings of 13th International Society for Music Information Retrieval Conference (ISMIR), Porto, Portugal pp. 169-174, 2012.&lt;br /&gt;
&lt;br /&gt;
F. Krebs, S. Boeck, and G. Widmer, &amp;quot;Rhythmic Pattern Modeling for Beat- and Downbeat Tracking in Musical Audio&amp;quot;, In Proceedings of 14th International Society for Music Information Retrieval Conference (ISMIR), Curitiba, Brazil, 2013.&lt;br /&gt;
&lt;br /&gt;
M. Mauch, C. Cannam, M. E. P. Davies, S. Dixon, C. Harte, S. Kolozali and D. Tidhar, &amp;quot;OMRAS2 Metadata Project 2009&amp;quot;, Late-breaking session at the 10th International Conference on Music Information Retrieval, 2009.&lt;br /&gt;
&lt;br /&gt;
A. Srinivasamurthy, A. Holzapfel, and Xavier Serra, &amp;quot;In Search of Automatic Rhythm Analysis Methods for Turkish and Indian Art Music&amp;quot;, Journal of New Music Research, vol. 43, no. 1, pp. 94-114, 2014.&lt;/div&gt;</summary>
		<author><name>Matthew Davies</name></author>
		
	</entry>
	<entry>
		<id>https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10049</id>
		<title>2014:Audio Downbeat Estimation</title>
		<link rel="alternate" type="text/html" href="https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10049"/>
		<updated>2014-05-23T15:10:56Z</updated>

		<summary type="html">&lt;p&gt;Matthew Davies: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Description ==&lt;br /&gt;
&lt;br /&gt;
'''This task is new for 2014!'''&lt;br /&gt;
&lt;br /&gt;
This text has been adapted from the Audio Beat Tracking Wiki page.  Please add your comments and discussion at the bottom of this page.&lt;br /&gt;
&lt;br /&gt;
The aim of the automatic downbeat estimation task is to identify the locations of downbeats in a collection of sound files. While this is similar to the Audio Beat Tracking task, here the aim is to find the first beat of each bar (measure) rather than all beat times. Algorithms are '''not''' required to estimate beat times in addition to downbeats.&lt;br /&gt;
&lt;br /&gt;
Submitted algorithms will be evaluated in terms of their accuracy in finding downbeat locations (only) as annotated by musical experts across several diverse datasets.&lt;br /&gt;
&lt;br /&gt;
== Data ==&lt;br /&gt;
&lt;br /&gt;
=== Collections ===&lt;br /&gt;
'''Ballroom'''&lt;br /&gt;
The ballroom dataset contains eight different dance styles (Cha Cha, Jive, Rumba, Samba, Tango, Viennese Waltz and Waltz). It consists of 697 excerpts of 30s in duration.&lt;br /&gt;
The dataset contains two different meters 3/4 and 4/4 - but all pieces have constant meter. For further information see Dixon et al (2004) and Krebs et al (2013).&lt;br /&gt;
Note, we are using the ground truth annotations from Krebs et al. (2013) available at https://github.com/CPJKU/BallroomAnnotations&lt;br /&gt;
&lt;br /&gt;
'''Isophonics (Beatles only)'''&lt;br /&gt;
The Beatles dataset from the Centre for Digital Music at Queen Mary, University of London (http://www.isophonics.net/), as also used for Audio Chord Estimation in MIREX for many years. Available from http://www.isophonics.net/. &lt;br /&gt;
This dataset contains 179 complete songs, the majority of which are in 4/4.&lt;br /&gt;
For further information see Mauch et al (2009).&lt;br /&gt;
&lt;br /&gt;
'''Turkish Data'''&lt;br /&gt;
The Turkish corpus is an extended version of the an&lt;br /&gt;
notated data used in Srinivasamurthy et al. (2014). It includes 82 excerpts of one&lt;br /&gt;
minute length each, and each piece belongs to one of three&lt;br /&gt;
rhythm classes that are referred to as usul in Turkish Art&lt;br /&gt;
music. 32 pieces are in the 9/8-usul Aksak, 20 pieces&lt;br /&gt;
in the 10/8-usul Curcuna, and 30 samples in the 8/8-usul&lt;br /&gt;
Düyek.&lt;br /&gt;
&lt;br /&gt;
'''Cretan Data'''&lt;br /&gt;
The corpus of Cretan music consists of 42 full length pieces of Cretan leaping dances. While there are several dances that differ in terms of their steps, the differences in&lt;br /&gt;
the sound are most noticeable in the melodic content, and all pieces can be considered to belong to one rhythmic style. All these dances are usually notated using a 2/4 time signature,&lt;br /&gt;
and the accompanying rhythmical patterns are usually played on a Cretan lute. While a variety of rhythmic patterns exist, they do not relate to a specific dance and can be&lt;br /&gt;
assumed to occur in all of the 42 songs in this corpus.&lt;br /&gt;
&lt;br /&gt;
'''HJDB''' (to be confirmed)&lt;br /&gt;
236 excerpts of Hardcore, Jungle and Drum and Bass music between 30s and 2 minutes in length. All excerpts are in 4/4 and have a constant tempo. &lt;br /&gt;
For further information see Hockman et al (2012).&lt;br /&gt;
&lt;br /&gt;
In total this makes 1234 excerpts (of which 259 are full length songs).&lt;br /&gt;
&lt;br /&gt;
=== Audio Formats ===&lt;br /&gt;
&lt;br /&gt;
The data are monophonic sound files, with the associated onset times and data about the annotation robustness.&lt;br /&gt;
&lt;br /&gt;
* CD-quality (PCM, 16-bit, 44100 Hz) for all except Ballroom (originally lower quality, but resampled to 44100 Hz)&lt;br /&gt;
* single channel (mono)&lt;br /&gt;
&lt;br /&gt;
== Submission Format ==&lt;br /&gt;
Submissions to this task will have to conform to a specified format detailed below. Submissions should be packaged and contain at least two files: The algorithm itself and a README containing contact information and detailing, in full, the use of the algorithm.&lt;br /&gt;
&lt;br /&gt;
=== Input Data ===&lt;br /&gt;
Participating algorithms will have to read audio in the following format:&lt;br /&gt;
&lt;br /&gt;
* Sample rate: 44.1 KHz&lt;br /&gt;
* Sample size: 16 bit&lt;br /&gt;
* Number of channels: 1 (mono)&lt;br /&gt;
* Encoding: WAV &lt;br /&gt;
&lt;br /&gt;
=== Output Data ===&lt;br /&gt;
&lt;br /&gt;
The downbeat estimation algorithms will return downbeat times in an ASCII text file for each input .wav audio file. The specification of this output file is immediately below.&lt;br /&gt;
&lt;br /&gt;
=== Output File Format (Audio Downbeat Estimation) ===&lt;br /&gt;
&lt;br /&gt;
The downbeat output file format is an ASCII text format. Each downbeat time is specified, in seconds, on its own line. Specifically, &lt;br /&gt;
&lt;br /&gt;
 &amp;lt;downbeat time (in seconds)&amp;gt;\n&lt;br /&gt;
&lt;br /&gt;
where \n denotes the end of line. The &amp;lt; and &amp;gt; characters are not included. An example output file would look something like:&lt;br /&gt;
&lt;br /&gt;
 0.243&lt;br /&gt;
 1.486&lt;br /&gt;
 2.729&lt;br /&gt;
&lt;br /&gt;
=== Algorithm Calling Format ===&lt;br /&gt;
&lt;br /&gt;
The submitted algorithm must take as arguments a SINGLE .wav file to perform the downbeat estimation as well as the full output path and filename of the output file. The ability to specify the output path and file name is essential. Denoting the input .wav file path and name as %input and the output file path and name as %output, a program called foobar could be called from the command-line as follows:&lt;br /&gt;
&lt;br /&gt;
 foobar %input %output&lt;br /&gt;
 foobar -i %input -o %output&lt;br /&gt;
&lt;br /&gt;
Moreover, if your submission takes additional parameters, such as a detection threshold, foobar could be called like:&lt;br /&gt;
&lt;br /&gt;
 foobar .1 %input %output&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output  &lt;br /&gt;
&lt;br /&gt;
If your submission is in MATLAB, it should be submitted as a function. Once again, the function must contain String inputs for the full path and names of the input and output files. Parameters could also be specified as input arguments of the function. For example: &lt;br /&gt;
&lt;br /&gt;
 foobar('%input','%output')&lt;br /&gt;
 foobar(.1,'%input','%output')&lt;br /&gt;
&lt;br /&gt;
=== README File ===&lt;br /&gt;
&lt;br /&gt;
A README file accompanying each submission should contain explicit instructions on how to to run the program (as well as contact information, etc.). In particular, each command line to run should be specified, using %input for the input sound file and %output for the resulting text file.&lt;br /&gt;
&lt;br /&gt;
For instance, to test the program foobar with different values for parameters param1, the README file would look like:&lt;br /&gt;
&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output&lt;br /&gt;
 foobar -param1 .15 -i %input -o %output&lt;br /&gt;
 foobar -param1 .2 -i %input -o %output&lt;br /&gt;
 foobar -param1 .25 -i %input -o %output&lt;br /&gt;
 foobar -param1 .3 -i %input -o %output&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
For a submission using MATLAB, the README file could look like:&lt;br /&gt;
&lt;br /&gt;
 matlab -r &amp;quot;foobar(.1,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.15,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.2,'%input','%output');quit;&amp;quot; &lt;br /&gt;
 matlab -r &amp;quot;foobar(.25,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.3,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
The different command lines to evaluate the performance of each parameter set over the whole database will be generated automatically from each line in the README file containing both '%input' and '%output' strings.&lt;br /&gt;
&lt;br /&gt;
== Evaluation Procedures ==&lt;br /&gt;
&lt;br /&gt;
For the evalution procedure we will use&lt;br /&gt;
*'''F-measure''' - the standard calculation as used in onset and beat tracking evaluation with a +/-70ms window, see Dixon (2007).&lt;br /&gt;
&lt;br /&gt;
== Time and hardware limits ==&lt;br /&gt;
Due to the potentially high number of participants in this and other audio tasks, hard limits on the runtime of submissions will be imposed.&lt;br /&gt;
&lt;br /&gt;
A hard limit of 24 hours will be imposed on analysis times. Submissions exceeding this limit may not receive a result.&lt;br /&gt;
&lt;br /&gt;
== Potential Participants ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
== Discussion ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
= Bibliography =&lt;br /&gt;
S. Dixon, F. Gouyon and G. Widmer, &amp;quot;Towards Characterisation of Music via Rhythmic Patterns&amp;quot;, In Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR 2004), pp 509-516.&lt;br /&gt;
&lt;br /&gt;
S. Dixon, &amp;quot;Evaluation of audio beat tracking system beatroot,&amp;quot; Journal of New Music Research, vol. 36, no. 1, pp. 39-51, 2007.&lt;br /&gt;
&lt;br /&gt;
J. A. Hockman, M. E. P. Davies, I. Fujinaga.&amp;quot;ONE IN THE JUNGLE: Downbeat Detection in Hardcore, Jungle, and Drum and Bass&amp;quot;, In Proceedings of 13th International Society for Music Information Retrieval Conference (ISMIR), Porto, Portugal pp. 169-174, 2012.&lt;br /&gt;
&lt;br /&gt;
F. Krebs, S. Boeck, and G. Widmer, &amp;quot;Rhythmic Pattern Modeling for Beat- and Downbeat Tracking in Musical Audio&amp;quot;, In Proceedings of 14th International Society for Music Information Retrieval Conference (ISMIR), Curitiba, Brazil, 2013.&lt;br /&gt;
&lt;br /&gt;
M. Mauch, C. Cannam, M. E. P. Davies, S. Dixon, C. Harte, S. Kolozali and D. Tidhar, &amp;quot;OMRAS2 Metadata Project 2009&amp;quot;, Late-breaking session at the 10th International Conference on Music Information Retrieval, 2009.&lt;br /&gt;
&lt;br /&gt;
A. Srinivasamurthy, A. Holzapfel, and Xavier Serra, &amp;quot;In Search of Automatic Rhythm Analysis Methods for Turkish and Indian Art Music&amp;quot;, Journal of New Music Research, vol. 43, no. 1, pp. 94-114, 2014.&lt;/div&gt;</summary>
		<author><name>Matthew Davies</name></author>
		
	</entry>
	<entry>
		<id>https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10048</id>
		<title>2014:Audio Downbeat Estimation</title>
		<link rel="alternate" type="text/html" href="https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10048"/>
		<updated>2014-05-23T15:03:30Z</updated>

		<summary type="html">&lt;p&gt;Matthew Davies: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Description ==&lt;br /&gt;
&lt;br /&gt;
'''This task is new for 2014!'''&lt;br /&gt;
&lt;br /&gt;
This text has been adapted from the Audio Beat Tracking Wiki page.  Please add your comments and discussion at the bottom of this page.&lt;br /&gt;
&lt;br /&gt;
The aim of the automatic downbeat estimation task is to identify the locations of downbeats in a collection of sound files. While this is similar to the Audio Beat Tracking task, here the aim is to find the first beat of each bar (measure) rather than all beat times. Algorithms are '''not''' required to estimate beat times in addition to downbeats.&lt;br /&gt;
&lt;br /&gt;
Submitted algorithms will be evaluated in terms of their accuracy in finding downbeat locations (only) as annotated by musical experts across several diverse datasets.&lt;br /&gt;
&lt;br /&gt;
== Data ==&lt;br /&gt;
&lt;br /&gt;
=== Collections ===&lt;br /&gt;
'''Ballroom'''&lt;br /&gt;
The ballroom dataset contains eight different dance styles (Cha Cha, Jive, Rumba, Samba, Tango, Viennese Waltz and Waltz). It consists of 697 excerpts of 30s in duration.&lt;br /&gt;
The dataset contains two different meters 3/4 and 4/4 - but all pieces have constant meter. For further information see Dixon et al (2004) and Krebs et al (2013).&lt;br /&gt;
Note, we are using the ground truth annotations from Krebs et al. (2013) available at https://github.com/CPJKU/BallroomAnnotations&lt;br /&gt;
&lt;br /&gt;
'''Isophonics (Beatles only)'''&lt;br /&gt;
The Beatles dataset from the Centre for Digital Music at Queen Mary, University of London (http://www.isophonics.net/), as also used for Audio Chord Estimation in MIREX for many years. Available from http://www.isophonics.net/. &lt;br /&gt;
This dataset contains 179 complete songs, the majority of which are in 4/4.&lt;br /&gt;
For further information see Mauch et al (2009).&lt;br /&gt;
&lt;br /&gt;
'''Turkish Data'''&lt;br /&gt;
80 1 minute excerpts in various time-signatures. &lt;br /&gt;
&lt;br /&gt;
'''Cretan Data'''&lt;br /&gt;
42 full songs in various time-signatures.&lt;br /&gt;
&lt;br /&gt;
'''HJDB''' (to be confirmed)&lt;br /&gt;
236 excerpts of Hardcore, Jungle and Drum and Bass music between 30s and 2 minutes in length. All excerpts are in 4/4 and have a constant tempo. &lt;br /&gt;
For further information see Hockman et al (2012).&lt;br /&gt;
&lt;br /&gt;
In total this makes 1234 excerpts (of which 259 are full length songs).&lt;br /&gt;
&lt;br /&gt;
=== Audio Formats ===&lt;br /&gt;
&lt;br /&gt;
The data are monophonic sound files, with the associated onset times and data about the annotation robustness.&lt;br /&gt;
&lt;br /&gt;
* CD-quality (PCM, 16-bit, 44100 Hz) for all except Ballroom (originally lower quality, but resampled to 44100 Hz)&lt;br /&gt;
* single channel (mono)&lt;br /&gt;
&lt;br /&gt;
== Submission Format ==&lt;br /&gt;
Submissions to this task will have to conform to a specified format detailed below. Submissions should be packaged and contain at least two files: The algorithm itself and a README containing contact information and detailing, in full, the use of the algorithm.&lt;br /&gt;
&lt;br /&gt;
=== Input Data ===&lt;br /&gt;
Participating algorithms will have to read audio in the following format:&lt;br /&gt;
&lt;br /&gt;
* Sample rate: 44.1 KHz&lt;br /&gt;
* Sample size: 16 bit&lt;br /&gt;
* Number of channels: 1 (mono)&lt;br /&gt;
* Encoding: WAV &lt;br /&gt;
&lt;br /&gt;
=== Output Data ===&lt;br /&gt;
&lt;br /&gt;
The downbeat estimation algorithms will return downbeat times in an ASCII text file for each input .wav audio file. The specification of this output file is immediately below.&lt;br /&gt;
&lt;br /&gt;
=== Output File Format (Audio Downbeat Estimation) ===&lt;br /&gt;
&lt;br /&gt;
The downbeat output file format is an ASCII text format. Each downbeat time is specified, in seconds, on its own line. Specifically, &lt;br /&gt;
&lt;br /&gt;
 &amp;lt;downbeat time (in seconds)&amp;gt;\n&lt;br /&gt;
&lt;br /&gt;
where \n denotes the end of line. The &amp;lt; and &amp;gt; characters are not included. An example output file would look something like:&lt;br /&gt;
&lt;br /&gt;
 0.243&lt;br /&gt;
 1.486&lt;br /&gt;
 2.729&lt;br /&gt;
&lt;br /&gt;
=== Algorithm Calling Format ===&lt;br /&gt;
&lt;br /&gt;
The submitted algorithm must take as arguments a SINGLE .wav file to perform the downbeat estimation as well as the full output path and filename of the output file. The ability to specify the output path and file name is essential. Denoting the input .wav file path and name as %input and the output file path and name as %output, a program called foobar could be called from the command-line as follows:&lt;br /&gt;
&lt;br /&gt;
 foobar %input %output&lt;br /&gt;
 foobar -i %input -o %output&lt;br /&gt;
&lt;br /&gt;
Moreover, if your submission takes additional parameters, such as a detection threshold, foobar could be called like:&lt;br /&gt;
&lt;br /&gt;
 foobar .1 %input %output&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output  &lt;br /&gt;
&lt;br /&gt;
If your submission is in MATLAB, it should be submitted as a function. Once again, the function must contain String inputs for the full path and names of the input and output files. Parameters could also be specified as input arguments of the function. For example: &lt;br /&gt;
&lt;br /&gt;
 foobar('%input','%output')&lt;br /&gt;
 foobar(.1,'%input','%output')&lt;br /&gt;
&lt;br /&gt;
=== README File ===&lt;br /&gt;
&lt;br /&gt;
A README file accompanying each submission should contain explicit instructions on how to to run the program (as well as contact information, etc.). In particular, each command line to run should be specified, using %input for the input sound file and %output for the resulting text file.&lt;br /&gt;
&lt;br /&gt;
For instance, to test the program foobar with different values for parameters param1, the README file would look like:&lt;br /&gt;
&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output&lt;br /&gt;
 foobar -param1 .15 -i %input -o %output&lt;br /&gt;
 foobar -param1 .2 -i %input -o %output&lt;br /&gt;
 foobar -param1 .25 -i %input -o %output&lt;br /&gt;
 foobar -param1 .3 -i %input -o %output&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
For a submission using MATLAB, the README file could look like:&lt;br /&gt;
&lt;br /&gt;
 matlab -r &amp;quot;foobar(.1,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.15,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.2,'%input','%output');quit;&amp;quot; &lt;br /&gt;
 matlab -r &amp;quot;foobar(.25,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.3,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
The different command lines to evaluate the performance of each parameter set over the whole database will be generated automatically from each line in the README file containing both '%input' and '%output' strings.&lt;br /&gt;
&lt;br /&gt;
== Evaluation Procedures ==&lt;br /&gt;
&lt;br /&gt;
For the evalution procedure we will use&lt;br /&gt;
*'''F-measure''' - the standard calculation as used in onset and beat tracking evaluation with a +/-70ms window, see Dixon (2007).&lt;br /&gt;
&lt;br /&gt;
== Time and hardware limits ==&lt;br /&gt;
Due to the potentially high number of participants in this and other audio tasks, hard limits on the runtime of submissions will be imposed.&lt;br /&gt;
&lt;br /&gt;
A hard limit of 24 hours will be imposed on analysis times. Submissions exceeding this limit may not receive a result.&lt;br /&gt;
&lt;br /&gt;
== Potential Participants ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
== Discussion ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
= Bibliography =&lt;br /&gt;
S. Dixon, F. Gouyon and G. Widmer, &amp;quot;Towards Characterisation of Music via Rhythmic Patterns&amp;quot;, In Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR 2004), pp 509-516.&lt;br /&gt;
&lt;br /&gt;
S. Dixon, &amp;quot;Evaluation of audio beat tracking system beatroot,&amp;quot; Journal of New Music Research, vol. 36, no. 1, pp. 39-51, 2007.&lt;br /&gt;
&lt;br /&gt;
J. A. Hockman, M. E. P. Davies, I. Fujinaga.&amp;quot;ONE IN THE JUNGLE: Downbeat Detection in Hardcore, Jungle, and Drum and Bass&amp;quot;, In Proceedings of 13th International Society for Music Information Retrieval Conference (ISMIR), Porto, Portugal pp. 169-174, 2012.&lt;br /&gt;
&lt;br /&gt;
F. Krebs, S. Boeck, and G. Widmer, &amp;quot;Rhythmic Pattern Modeling for Beat- and Downbeat Tracking in Musical Audio&amp;quot;, In Proceedings of 14th International Society for Music Information Retrieval Conference (ISMIR), Curitiba, Brazil, 2013.&lt;br /&gt;
&lt;br /&gt;
M. Mauch, C. Cannam, M. E. P. Davies, S. Dixon, C. Harte, S. Kolozali and D. Tidhar, &amp;quot;OMRAS2 Metadata Project 2009&amp;quot;, Late-breaking session at the 10th International Conference on Music Information Retrieval, 2009.&lt;/div&gt;</summary>
		<author><name>Matthew Davies</name></author>
		
	</entry>
	<entry>
		<id>https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10047</id>
		<title>2014:Audio Downbeat Estimation</title>
		<link rel="alternate" type="text/html" href="https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10047"/>
		<updated>2014-05-23T14:54:07Z</updated>

		<summary type="html">&lt;p&gt;Matthew Davies: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Description ==&lt;br /&gt;
&lt;br /&gt;
'''This task is new for 2014!'''&lt;br /&gt;
&lt;br /&gt;
This text has been adapted from the Audio Beat Tracking Wiki page.  Please add your comments and discussion at the bottom of this page.&lt;br /&gt;
&lt;br /&gt;
The aim of the automatic downbeat estimation task is to identify the locations of downbeats in a collection of sound files. While this is similar to the Audio Beat Tracking task, here the aim is to find the first beat of each bar (measure) rather than all beat times. Algorithms are '''not''' required to estimate beat times in addition to downbeats.&lt;br /&gt;
&lt;br /&gt;
Submitted algorithms will be evaluated in terms of their accuracy in finding downbeat locations (only) as annotated by musical experts across several diverse datasets.&lt;br /&gt;
&lt;br /&gt;
== Data ==&lt;br /&gt;
&lt;br /&gt;
=== Collections ===&lt;br /&gt;
'''Ballroom'''&lt;br /&gt;
The ballroom dataset contains eight different dance styles (Cha Cha, Jive, Rumba, Samba, Tango, Viennese Waltz and Waltz). It consists of 697 excerpts of 30s in duration.&lt;br /&gt;
The dataset contains two different meters 3/4 and 4/4 - but all pieces have constant meter. For further information see Dixon et al (2004) and Krebs et al (2013).&lt;br /&gt;
Note, we are using the ground truth annotations from Krebs et al. (2013) available at https://github.com/CPJKU/BallroomAnnotations&lt;br /&gt;
&lt;br /&gt;
'''Isophonics (Beatles only)'''&lt;br /&gt;
The Beatles dataset from the Centre for Digital Music at Queen Mary, University of London (http://www.isophonics.net/), as also used for Audio Chord Estimation in MIREX for many years. Available from http://www.isophonics.net/. &lt;br /&gt;
This dataset contains 179 complete songs, the majority of which are in 4/4.&lt;br /&gt;
For further information see Mauch et al (2009).&lt;br /&gt;
&lt;br /&gt;
'''Turkish Data'''&lt;br /&gt;
80 1 minute excerpts in various time-signatures. &lt;br /&gt;
&lt;br /&gt;
'''Cretan Data'''&lt;br /&gt;
42 full songs in various time-signatures.&lt;br /&gt;
&lt;br /&gt;
'''HJDB''' (to be confirmed)&lt;br /&gt;
236 excerpts of Hardcore, Jungle and Drum and Bass music between 30s and 2 minutes in length. All excerpts are in 4/4 and have a constant tempo. &lt;br /&gt;
For further information see Hockman et al (2012).&lt;br /&gt;
&lt;br /&gt;
In total this makes 1234 excerpts (of which 259 are full length songs).&lt;br /&gt;
&lt;br /&gt;
=== Audio Formats ===&lt;br /&gt;
&lt;br /&gt;
The data are monophonic sound files, with the associated onset times and data about the annotation robustness.&lt;br /&gt;
&lt;br /&gt;
* CD-quality (PCM, 16-bit, 44100 Hz) for all except Ballroom (originally lower quality, but resampled to 44100 Hz)&lt;br /&gt;
* single channel (mono)&lt;br /&gt;
&lt;br /&gt;
== Submission Format ==&lt;br /&gt;
Submissions to this task will have to conform to a specified format detailed below. Submissions should be packaged and contain at least two files: The algorithm itself and a README containing contact information and detailing, in full, the use of the algorithm.&lt;br /&gt;
&lt;br /&gt;
=== Input Data ===&lt;br /&gt;
Participating algorithms will have to read audio in the following format:&lt;br /&gt;
&lt;br /&gt;
* Sample rate: 44.1 KHz&lt;br /&gt;
* Sample size: 16 bit&lt;br /&gt;
* Number of channels: 1 (mono)&lt;br /&gt;
* Encoding: WAV &lt;br /&gt;
&lt;br /&gt;
=== Output Data ===&lt;br /&gt;
&lt;br /&gt;
The downbeat estimation algorithms will return downbeat times in an ASCII text file for each input .wav audio file. The specification of this output file is immediately below.&lt;br /&gt;
&lt;br /&gt;
=== Output File Format (Audio Downbeat Estimation) ===&lt;br /&gt;
&lt;br /&gt;
The downbeat output file format is an ASCII text format. Each downbeat time is specified, in seconds, on its own line. Specifically, &lt;br /&gt;
&lt;br /&gt;
 &amp;lt;downbeat time (in seconds)&amp;gt;\n&lt;br /&gt;
&lt;br /&gt;
where \n denotes the end of line. The &amp;lt; and &amp;gt; characters are not included. An example output file would look something like:&lt;br /&gt;
&lt;br /&gt;
 0.243&lt;br /&gt;
 1.486&lt;br /&gt;
 2.729&lt;br /&gt;
&lt;br /&gt;
=== Algorithm Calling Format ===&lt;br /&gt;
&lt;br /&gt;
The submitted algorithm must take as arguments a SINGLE .wav file to perform the downbeat estimation as well as the full output path and filename of the output file. The ability to specify the output path and file name is essential. Denoting the input .wav file path and name as %input and the output file path and name as %output, a program called foobar could be called from the command-line as follows:&lt;br /&gt;
&lt;br /&gt;
 foobar %input %output&lt;br /&gt;
 foobar -i %input -o %output&lt;br /&gt;
&lt;br /&gt;
Moreover, if your submission takes additional parameters, such as a detection threshold, foobar could be called like:&lt;br /&gt;
&lt;br /&gt;
 foobar .1 %input %output&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output  &lt;br /&gt;
&lt;br /&gt;
If your submission is in MATLAB, it should be submitted as a function. Once again, the function must contain String inputs for the full path and names of the input and output files. Parameters could also be specified as input arguments of the function. For example: &lt;br /&gt;
&lt;br /&gt;
 foobar('%input','%output')&lt;br /&gt;
 foobar(.1,'%input','%output')&lt;br /&gt;
&lt;br /&gt;
=== README File ===&lt;br /&gt;
&lt;br /&gt;
A README file accompanying each submission should contain explicit instructions on how to to run the program (as well as contact information, etc.). In particular, each command line to run should be specified, using %input for the input sound file and %output for the resulting text file.&lt;br /&gt;
&lt;br /&gt;
For instance, to test the program foobar with different values for parameters param1, the README file would look like:&lt;br /&gt;
&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output&lt;br /&gt;
 foobar -param1 .15 -i %input -o %output&lt;br /&gt;
 foobar -param1 .2 -i %input -o %output&lt;br /&gt;
 foobar -param1 .25 -i %input -o %output&lt;br /&gt;
 foobar -param1 .3 -i %input -o %output&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
For a submission using MATLAB, the README file could look like:&lt;br /&gt;
&lt;br /&gt;
 matlab -r &amp;quot;foobar(.1,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.15,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.2,'%input','%output');quit;&amp;quot; &lt;br /&gt;
 matlab -r &amp;quot;foobar(.25,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.3,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
The different command lines to evaluate the performance of each parameter set over the whole database will be generated automatically from each line in the README file containing both '%input' and '%output' strings.&lt;br /&gt;
&lt;br /&gt;
== Evaluation Procedures ==&lt;br /&gt;
&lt;br /&gt;
For the evalution procedure we will use&lt;br /&gt;
*'''F-measure''' - the standard calculation as used in onset and beat tracking evaluation with a +/-70ms window, see Dixon (2007).&lt;br /&gt;
&lt;br /&gt;
== Time and hardware limits ==&lt;br /&gt;
Due to the potentially high number of participants in this and other audio tasks, hard limits on the runtime of submissions will be imposed.&lt;br /&gt;
&lt;br /&gt;
A hard limit of 24 hours will be imposed on analysis times. Submissions exceeding this limit may not receive a result.&lt;br /&gt;
&lt;br /&gt;
== Potential Participants ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
== Discussion ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
= Bibliography =&lt;br /&gt;
S. Dixon, F. Gouyon and G. Widmer, &amp;quot;Towards Characterisation of Music via Rhythmic Patterns&amp;quot;, In 5th International Conference on Music Information Retrieval (ISMIR 2004), pp 509-516.&lt;br /&gt;
&lt;br /&gt;
S. Dixon, &amp;quot;Evaluation of audio beat tracking system beatroot,&amp;quot; Journal of New Music Research, vol. 36, no. 1, pp. 39-51, 2007.&lt;br /&gt;
&lt;br /&gt;
J. A. Hockman, M. E. P. Davies, I. Fujinaga.&amp;quot;ONE IN THE JUNGLE: Downbeat Detection in Hardcore, Jungle, and Drum and Bass&amp;quot;, In Proc of ISMIR 2012, Porto, Portugal pp. 169-174, 2012.&lt;br /&gt;
&lt;br /&gt;
F. Krebs, S. Boeck, and G. Widmer, &amp;quot;Rhythmic Pattern Modeling for Beat- and Downbeat Tracking in Musical Audio&amp;quot;, In Proceedings of 14th International Society for Music Information Retrieval Conference (ISMIR), Curitiba, Brazil, 2013.&lt;br /&gt;
&lt;br /&gt;
M. Mauch, C. Cannam, M. E. P. Davies, S. Dixon, C. Harte, S. Kolozali and D. Tidhar, &amp;quot;OMRAS2 Metadata Project 2009&amp;quot;, Late-breaking session at the 10th International Conference on Music Information Retrieval, 2009.&lt;/div&gt;</summary>
		<author><name>Matthew Davies</name></author>
		
	</entry>
	<entry>
		<id>https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10046</id>
		<title>2014:Audio Downbeat Estimation</title>
		<link rel="alternate" type="text/html" href="https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10046"/>
		<updated>2014-05-23T14:53:11Z</updated>

		<summary type="html">&lt;p&gt;Matthew Davies: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Description ==&lt;br /&gt;
&lt;br /&gt;
'''This task is new for 2014!'''&lt;br /&gt;
&lt;br /&gt;
This text has been adapted from the Audio Beat Tracking Wiki page.  Please add your comments and discussion at the bottom of this page.&lt;br /&gt;
&lt;br /&gt;
The aim of the automatic downbeat estimation task is to identify the locations of downbeats in a collection of sound files. While this is similar to the Audio Beat Tracking task, here the aim is to find the first beat of each bar (measure) rather than all beat times. Algorithms are '''not''' required to estimate beat times in addition to downbeats.&lt;br /&gt;
&lt;br /&gt;
Submitted algorithms will be evaluated in terms of their accuracy in finding downbeat locations (only) as annotated by musical experts across several diverse datasets.&lt;br /&gt;
&lt;br /&gt;
== Data ==&lt;br /&gt;
&lt;br /&gt;
=== Collections ===&lt;br /&gt;
'''Ballroom'''&lt;br /&gt;
The ballroom dataset contains eight different dance styles (Cha Cha, Jive, Rumba, Samba, Tango, Viennese Waltz and Waltz). It consists of 697 excerpts of 30s in duration.&lt;br /&gt;
The dataset contains two different meters 3/4 and 4/4 - but all pieces have constant meter. For further information see Dixon et al (2004) and Krebs et al (2013).&lt;br /&gt;
Note, we are using the ground truth annotations from Krebs et al. (2013) available at https://github.com/CPJKU/BallroomAnnotations&lt;br /&gt;
&lt;br /&gt;
'''Isophonics (Beatles only)'''&lt;br /&gt;
The Beatles dataset from the Centre for Digital Music at Queen Mary, University of London (http://www.isophonics.net/), as also used for Audio Chord Estimation in MIREX for many years. Available from http://www.isophonics.net/. &lt;br /&gt;
This dataset contains 179 complete songs, the majority of which are in 4/4.&lt;br /&gt;
For further information see Mauch et al (2009).&lt;br /&gt;
&lt;br /&gt;
'''Turkish Data'''&lt;br /&gt;
80 1 minute excerpts in various time-signatures. &lt;br /&gt;
&lt;br /&gt;
'''Cretan Data'''&lt;br /&gt;
42 full songs in various time-signatures.&lt;br /&gt;
&lt;br /&gt;
'''HJDB''' (to be confirmed)&lt;br /&gt;
236 excerpts of Hardcore, Jungle and Drum and Bass music between 30s and 2 minutes in length. All excerpts are in 4/4 and have a constant tempo. &lt;br /&gt;
For further information see Hockman et al (2012).&lt;br /&gt;
&lt;br /&gt;
In total this makes 1234 excerpts (of which 259 are full length songs).&lt;br /&gt;
&lt;br /&gt;
=== Audio Formats ===&lt;br /&gt;
&lt;br /&gt;
The data are monophonic sound files, with the associated onset times and data about the annotation robustness.&lt;br /&gt;
&lt;br /&gt;
* CD-quality (PCM, 16-bit, 44100 Hz) for all except Ballroom (originally lower quality, but resampled to 44100 Hz)&lt;br /&gt;
* single channel (mono)&lt;br /&gt;
&lt;br /&gt;
== Submission Format ==&lt;br /&gt;
Submissions to this task will have to conform to a specified format detailed below. Submissions should be packaged and contain at least two files: The algorithm itself and a README containing contact information and detailing, in full, the use of the algorithm.&lt;br /&gt;
&lt;br /&gt;
=== Input Data ===&lt;br /&gt;
Participating algorithms will have to read audio in the following format:&lt;br /&gt;
&lt;br /&gt;
* Sample rate: 44.1 KHz&lt;br /&gt;
* Sample size: 16 bit&lt;br /&gt;
* Number of channels: 1 (mono)&lt;br /&gt;
* Encoding: WAV &lt;br /&gt;
&lt;br /&gt;
=== Output Data ===&lt;br /&gt;
&lt;br /&gt;
The downbeat estimation algorithms will return downbeat times in an ASCII text file for each input .wav audio file. The specification of this output file is immediately below.&lt;br /&gt;
&lt;br /&gt;
=== Output File Format (Audio Downbeat Estimation) ===&lt;br /&gt;
&lt;br /&gt;
The downbeat output file format is an ASCII text format. Each downbeat time is specified, in seconds, on its own line. Specifically, &lt;br /&gt;
&lt;br /&gt;
 &amp;lt;downbeat time (in seconds)&amp;gt;\n&lt;br /&gt;
&lt;br /&gt;
where \n denotes the end of line. The &amp;lt; and &amp;gt; characters are not included. An example output file would look something like:&lt;br /&gt;
&lt;br /&gt;
 0.243&lt;br /&gt;
 1.486&lt;br /&gt;
 2.729&lt;br /&gt;
&lt;br /&gt;
=== Algorithm Calling Format ===&lt;br /&gt;
&lt;br /&gt;
The submitted algorithm must take as arguments a SINGLE .wav file to perform the downbeat estimation as well as the full output path and filename of the output file. The ability to specify the output path and file name is essential. Denoting the input .wav file path and name as %input and the output file path and name as %output, a program called foobar could be called from the command-line as follows:&lt;br /&gt;
&lt;br /&gt;
 foobar %input %output&lt;br /&gt;
 foobar -i %input -o %output&lt;br /&gt;
&lt;br /&gt;
Moreover, if your submission takes additional parameters, such as a detection threshold, foobar could be called like:&lt;br /&gt;
&lt;br /&gt;
 foobar .1 %input %output&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output  &lt;br /&gt;
&lt;br /&gt;
If your submission is in MATLAB, it should be submitted as a function. Once again, the function must contain String inputs for the full path and names of the input and output files. Parameters could also be specified as input arguments of the function. For example: &lt;br /&gt;
&lt;br /&gt;
 foobar('%input','%output')&lt;br /&gt;
 foobar(.1,'%input','%output')&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== README File ===&lt;br /&gt;
&lt;br /&gt;
A README file accompanying each submission should contain explicit instructions on how to to run the program (as well as contact information, etc.). In particular, each command line to run should be specified, using %input for the input sound file and %output for the resulting text file.&lt;br /&gt;
&lt;br /&gt;
For instance, to test the program foobar with different values for parameters param1, the README file would look like:&lt;br /&gt;
&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output&lt;br /&gt;
 foobar -param1 .15 -i %input -o %output&lt;br /&gt;
 foobar -param1 .2 -i %input -o %output&lt;br /&gt;
 foobar -param1 .25 -i %input -o %output&lt;br /&gt;
 foobar -param1 .3 -i %input -o %output&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
For a submission using MATLAB, the README file could look like:&lt;br /&gt;
&lt;br /&gt;
 matlab -r &amp;quot;foobar(.1,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.15,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.2,'%input','%output');quit;&amp;quot; &lt;br /&gt;
 matlab -r &amp;quot;foobar(.25,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.3,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
The different command lines to evaluate the performance of each parameter set over the whole database will be generated automatically from each line in the README file containing both '%input' and '%output' strings.&lt;br /&gt;
&lt;br /&gt;
== Evaluation Procedures ==&lt;br /&gt;
&lt;br /&gt;
For the evalution procedure we will use&lt;br /&gt;
*'''F-measure''' - the standard calculation as used in onset and beat tracking evaluation with a +/-70ms window, see Dixon (2007).&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Time and hardware limits ==&lt;br /&gt;
Due to the potentially high number of participants in this and other audio tasks, hard limits on the runtime of submissions will be imposed.&lt;br /&gt;
&lt;br /&gt;
A hard limit of 24 hours will be imposed on analysis times. Submissions exceeding this limit may not receive a result.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Potential Participants ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
== Discussion ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
= Bibliography =&lt;br /&gt;
S. Dixon, F. Gouyon and G. Widmer, &amp;quot;Towards Characterisation of Music via Rhythmic Patterns&amp;quot;, In 5th International Conference on Music Information Retrieval (ISMIR 2004), pp 509-516.&lt;br /&gt;
&lt;br /&gt;
S. Dixon, &amp;quot;Evaluation of audio beat tracking system beatroot,&amp;quot; Journal of New Music Research, vol. 36, no. 1, pp. 39-51, 2007.&lt;br /&gt;
&lt;br /&gt;
J. A. Hockman, M. E. P. Davies, I. Fujinaga.&amp;quot;ONE IN THE JUNGLE: Downbeat Detection in Hardcore, Jungle, and Drum and Bass&amp;quot;, In Proc of ISMIR 2012, Porto, Portugal pp. 169-174, 2012.&lt;br /&gt;
&lt;br /&gt;
F. Krebs, S. Boeck, and G. Widmer, &amp;quot;Rhythmic Pattern Modeling for Beat- and Downbeat Tracking in Musical Audio&amp;quot;, In Proceedings of 14th International Society for Music Information Retrieval Conference (ISMIR), Curitiba, Brazil, 2013.&lt;br /&gt;
&lt;br /&gt;
M. Mauch, C. Cannam, M. E. P. Davies, S. Dixon, C. Harte, S. Kolozali and D. Tidhar, &amp;quot;OMRAS2 Metadata Project 2009&amp;quot;, Late-breaking session at the 10th International Conference on Music Information Retrieval, 2009.&lt;/div&gt;</summary>
		<author><name>Matthew Davies</name></author>
		
	</entry>
	<entry>
		<id>https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10045</id>
		<title>2014:Audio Downbeat Estimation</title>
		<link rel="alternate" type="text/html" href="https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10045"/>
		<updated>2014-05-23T14:52:38Z</updated>

		<summary type="html">&lt;p&gt;Matthew Davies: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Description ==&lt;br /&gt;
&lt;br /&gt;
'''This task is new for 2014!'''&lt;br /&gt;
&lt;br /&gt;
This text has been adapted from the Audio Beat Tracking Wiki page.  Please add your comments and discussion at the bottom of this page.&lt;br /&gt;
&lt;br /&gt;
The aim of the automatic downbeat estimation task is to identify the locations of downbeats in a collection of sound files. While this is similar to the Audio Beat Tracking task, here the aim is to find the first beat of each bar (measure) rather than all beat times. Algorithms are '''not''' required to estimate beat times in addition to downbeats.&lt;br /&gt;
&lt;br /&gt;
Submitted algorithms will be evaluated in terms of their accuracy in finding downbeat locations (only) as annotated by musical experts across several diverse datasets.&lt;br /&gt;
&lt;br /&gt;
== Data ==&lt;br /&gt;
&lt;br /&gt;
=== Collections ===&lt;br /&gt;
'''Ballroom'''&lt;br /&gt;
The ballroom dataset contains eight different dance styles (Cha Cha, Jive, Rumba, Samba, Tango, Viennese Waltz and Waltz). It consists of 697 excerpts of 30s in duration.&lt;br /&gt;
The dataset contains two different meters 3/4 and 4/4 - but all pieces have constant meter. For further information see Dixon et al (2004) and Krebs et al (2013).&lt;br /&gt;
Note, we are using the ground truth annotations from Krebs et al. (2013) available at https://github.com/CPJKU/BallroomAnnotations&lt;br /&gt;
&lt;br /&gt;
'''Isophonics (Beatles only)'''&lt;br /&gt;
The Beatles dataset from the Centre for Digital Music at Queen Mary, University of London (http://www.isophonics.net/), as also used for Audio Chord Estimation in MIREX for many years. Available from http://www.isophonics.net/. &lt;br /&gt;
This dataset contains 179 complete songs, the majority of which are in 4/4.&lt;br /&gt;
For further information see Mauch et al (2009).&lt;br /&gt;
&lt;br /&gt;
'''Turkish Data'''&lt;br /&gt;
80 1 minute excerpts in various time-signatures. &lt;br /&gt;
&lt;br /&gt;
'''Cretan Data'''&lt;br /&gt;
42 full songs in various time-signatures.&lt;br /&gt;
&lt;br /&gt;
'''HJDB''' (to be confirmed)&lt;br /&gt;
236 excerpts of Hardcore, Jungle and Drum and Bass music between 30s and 2 minutes in length. All excerpts are in 4/4 and have a constant tempo. &lt;br /&gt;
For further information see Hockman et al (2012).&lt;br /&gt;
&lt;br /&gt;
In total this makes 1234 excerpts (of which 259 are full length songs).&lt;br /&gt;
&lt;br /&gt;
=== Audio Formats ===&lt;br /&gt;
&lt;br /&gt;
The data are monophonic sound files, with the associated onset times and data about the annotation robustness.&lt;br /&gt;
&lt;br /&gt;
* CD-quality (PCM, 16-bit, 44100 Hz) for all except Ballroom (originally lower quality, but resampled to 44100 Hz)&lt;br /&gt;
* single channel (mono)&lt;br /&gt;
&lt;br /&gt;
== Submission Format ==&lt;br /&gt;
Submissions to this task will have to conform to a specified format detailed below. Submissions should be packaged and contain at least two files: The algorithm itself and a README containing contact information and detailing, in full, the use of the algorithm.&lt;br /&gt;
&lt;br /&gt;
=== Input Data ===&lt;br /&gt;
Participating algorithms will have to read audio in the following format:&lt;br /&gt;
&lt;br /&gt;
* Sample rate: 44.1 KHz&lt;br /&gt;
* Sample size: 16 bit&lt;br /&gt;
* Number of channels: 1 (mono)&lt;br /&gt;
* Encoding: WAV &lt;br /&gt;
&lt;br /&gt;
=== Output Data ===&lt;br /&gt;
&lt;br /&gt;
The downbeat estimation algorithms will return downbeat times in an ASCII text file for each input .wav audio file. The specification of this output file is immediately below.&lt;br /&gt;
&lt;br /&gt;
=== Output File Format (Audio Downbeat Estimation) ===&lt;br /&gt;
&lt;br /&gt;
The downbeat output file format is an ASCII text format. Each downbeat time is specified, in seconds, on its own line. Specifically, &lt;br /&gt;
&lt;br /&gt;
 &amp;lt;downbeat time(in seconds)&amp;gt;\n&lt;br /&gt;
&lt;br /&gt;
where \n denotes the end of line. The &amp;lt; and &amp;gt; characters are not included. An example output file would look something like:&lt;br /&gt;
&lt;br /&gt;
 0.243&lt;br /&gt;
 1.486&lt;br /&gt;
 2.729&lt;br /&gt;
&lt;br /&gt;
=== Algorithm Calling Format ===&lt;br /&gt;
&lt;br /&gt;
The submitted algorithm must take as arguments a SINGLE .wav file to perform the downbeat estimation as well as the full output path and filename of the output file. The ability to specify the output path and file name is essential. Denoting the input .wav file path and name as %input and the output file path and name as %output, a program called foobar could be called from the command-line as follows:&lt;br /&gt;
&lt;br /&gt;
 foobar %input %output&lt;br /&gt;
 foobar -i %input -o %output&lt;br /&gt;
&lt;br /&gt;
Moreover, if your submission takes additional parameters, such as a detection threshold, foobar could be called like:&lt;br /&gt;
&lt;br /&gt;
 foobar .1 %input %output&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output  &lt;br /&gt;
&lt;br /&gt;
If your submission is in MATLAB, it should be submitted as a function. Once again, the function must contain String inputs for the full path and names of the input and output files. Parameters could also be specified as input arguments of the function. For example: &lt;br /&gt;
&lt;br /&gt;
 foobar('%input','%output')&lt;br /&gt;
 foobar(.1,'%input','%output')&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== README File ===&lt;br /&gt;
&lt;br /&gt;
A README file accompanying each submission should contain explicit instructions on how to to run the program (as well as contact information, etc.). In particular, each command line to run should be specified, using %input for the input sound file and %output for the resulting text file.&lt;br /&gt;
&lt;br /&gt;
For instance, to test the program foobar with different values for parameters param1, the README file would look like:&lt;br /&gt;
&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output&lt;br /&gt;
 foobar -param1 .15 -i %input -o %output&lt;br /&gt;
 foobar -param1 .2 -i %input -o %output&lt;br /&gt;
 foobar -param1 .25 -i %input -o %output&lt;br /&gt;
 foobar -param1 .3 -i %input -o %output&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
For a submission using MATLAB, the README file could look like:&lt;br /&gt;
&lt;br /&gt;
 matlab -r &amp;quot;foobar(.1,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.15,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.2,'%input','%output');quit;&amp;quot; &lt;br /&gt;
 matlab -r &amp;quot;foobar(.25,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.3,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
The different command lines to evaluate the performance of each parameter set over the whole database will be generated automatically from each line in the README file containing both '%input' and '%output' strings.&lt;br /&gt;
&lt;br /&gt;
== Evaluation Procedures ==&lt;br /&gt;
&lt;br /&gt;
For the evalution procedure we will use&lt;br /&gt;
*'''F-measure''' - the standard calculation as used in onset and beat tracking evaluation with a +/-70ms window, see Dixon (2007).&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Time and hardware limits ==&lt;br /&gt;
Due to the potentially high number of participants in this and other audio tasks, hard limits on the runtime of submissions will be imposed.&lt;br /&gt;
&lt;br /&gt;
A hard limit of 24 hours will be imposed on analysis times. Submissions exceeding this limit may not receive a result.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Potential Participants ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
== Discussion ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
= Bibliography =&lt;br /&gt;
S. Dixon, F. Gouyon and G. Widmer, &amp;quot;Towards Characterisation of Music via Rhythmic Patterns&amp;quot;, In 5th International Conference on Music Information Retrieval (ISMIR 2004), pp 509-516.&lt;br /&gt;
&lt;br /&gt;
S. Dixon, &amp;quot;Evaluation of audio beat tracking system beatroot,&amp;quot; Journal of New Music Research, vol. 36, no. 1, pp. 39-51, 2007.&lt;br /&gt;
&lt;br /&gt;
J. A. Hockman, M. E. P. Davies, I. Fujinaga.&amp;quot;ONE IN THE JUNGLE: Downbeat Detection in Hardcore, Jungle, and Drum and Bass&amp;quot;, In Proc of ISMIR 2012, Porto, Portugal pp. 169-174, 2012.&lt;br /&gt;
&lt;br /&gt;
F. Krebs, S. Boeck, and G. Widmer, &amp;quot;Rhythmic Pattern Modeling for Beat- and Downbeat Tracking in Musical Audio&amp;quot;, In Proceedings of 14th International Society for Music Information Retrieval Conference (ISMIR), Curitiba, Brazil, 2013.&lt;br /&gt;
&lt;br /&gt;
M. Mauch, C. Cannam, M. E. P. Davies, S. Dixon, C. Harte, S. Kolozali and D. Tidhar, &amp;quot;OMRAS2 Metadata Project 2009&amp;quot;, Late-breaking session at the 10th International Conference on Music Information Retrieval, 2009.&lt;/div&gt;</summary>
		<author><name>Matthew Davies</name></author>
		
	</entry>
	<entry>
		<id>https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10044</id>
		<title>2014:Audio Downbeat Estimation</title>
		<link rel="alternate" type="text/html" href="https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10044"/>
		<updated>2014-05-23T14:50:22Z</updated>

		<summary type="html">&lt;p&gt;Matthew Davies: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Description ==&lt;br /&gt;
&lt;br /&gt;
'''This task is new for 2014!'''&lt;br /&gt;
&lt;br /&gt;
This text has been adapted from the Audio Beat Tracking Wiki page.  Please add your comments and discussion at the bottom of this page.&lt;br /&gt;
&lt;br /&gt;
The aim of the automatic downbeat estimation task is to identify the locations of downbeats in a collection of sound files. While this is similar to the Audio Beat Tracking task, here the aim is to find the first beat of each bar (measure) rather than all beat times. Algorithms are '''not''' required to estimate beat times in addition to downbeats.&lt;br /&gt;
&lt;br /&gt;
Submitted algorithms will be evaluated in terms of their accuracy in finding downbeat locations (only) as annotated by musical experts across several diverse datasets.&lt;br /&gt;
&lt;br /&gt;
== Data ==&lt;br /&gt;
&lt;br /&gt;
=== Collections ===&lt;br /&gt;
'''Ballroom'''&lt;br /&gt;
The ballroom dataset contains eight different dance styles (Cha Cha, Jive, Rumba, Samba, Tango, Viennese Waltz and Waltz). It consists of 697 excerpts of 30s in duration.&lt;br /&gt;
The dataset contains two different meters 3/4 and 4/4 - but all pieces have constant meter. For further information see Dixon et al (2004) and Krebs et al (2013).&lt;br /&gt;
Note, we are using the ground truth annotations from Krebs et al. (2013) available at https://github.com/CPJKU/BallroomAnnotations&lt;br /&gt;
&lt;br /&gt;
'''Isophonics (Beatles only)'''&lt;br /&gt;
The Beatles dataset from the Centre for Digital Music at Queen Mary, University of London (http://www.isophonics.net/), as also used for Audio Chord Estimation in MIREX for many years. Available from http://www.isophonics.net/. &lt;br /&gt;
This dataset contains 179 complete songs, the majority of which are in 4/4.&lt;br /&gt;
For further information see Mauch et al (2009).&lt;br /&gt;
&lt;br /&gt;
'''Turkish Data'''&lt;br /&gt;
80 1 minute excerpts in various time-signatures. &lt;br /&gt;
&lt;br /&gt;
'''Cretan Data'''&lt;br /&gt;
42 full songs in various time-signature.&lt;br /&gt;
&lt;br /&gt;
'''HJDB''' (to be confirmed)&lt;br /&gt;
236 excerpts of Hardcore, Jungle and Drum and Bass music between 30s and 2 minutes in length. All excerpts are in 4/4 and have a constant tempo. &lt;br /&gt;
For further information see Hockman et al (2012).&lt;br /&gt;
&lt;br /&gt;
In total this makes 1234 excerpts (of which 259 are full length songs).&lt;br /&gt;
&lt;br /&gt;
=== Audio Formats ===&lt;br /&gt;
&lt;br /&gt;
The data are monophonic sound files, with the associated onset times and data about the annotation robustness.&lt;br /&gt;
&lt;br /&gt;
* CD-quality (PCM, 16-bit, 44100 Hz) for all except Ballroom (originally lower quality, but resampled to 44100 Hz)&lt;br /&gt;
* single channel (mono)&lt;br /&gt;
&lt;br /&gt;
== Submission Format ==&lt;br /&gt;
Submissions to this task will have to conform to a specified format detailed below. Submissions should be packaged and contain at least two files: The algorithm itself and a README containing contact information and detailing, in full, the use of the algorithm.&lt;br /&gt;
&lt;br /&gt;
=== Input Data ===&lt;br /&gt;
Participating algorithms will have to read audio in the following format:&lt;br /&gt;
&lt;br /&gt;
* Sample rate: 44.1 KHz&lt;br /&gt;
* Sample size: 16 bit&lt;br /&gt;
* Number of channels: 1 (mono)&lt;br /&gt;
* Encoding: WAV &lt;br /&gt;
&lt;br /&gt;
=== Output Data ===&lt;br /&gt;
&lt;br /&gt;
The downbeat estimation algorithms will return downbeat times in an ASCII text file for each input .wav audio file. The specification of this output file is immediately below.&lt;br /&gt;
&lt;br /&gt;
=== Output File Format (Audio Downbeat Estimation) ===&lt;br /&gt;
&lt;br /&gt;
The downbeat output file format is an ASCII text format. Each downbeat time is specified, in seconds, on its own line. Specifically, &lt;br /&gt;
&lt;br /&gt;
 &amp;lt;downbeat time(in seconds)&amp;gt;\n&lt;br /&gt;
&lt;br /&gt;
where \n denotes the end of line. The &amp;lt; and &amp;gt; characters are not included. An example output file would look something like:&lt;br /&gt;
&lt;br /&gt;
 0.243&lt;br /&gt;
 1.486&lt;br /&gt;
 2.729&lt;br /&gt;
&lt;br /&gt;
=== Algorithm Calling Format ===&lt;br /&gt;
&lt;br /&gt;
The submitted algorithm must take as arguments a SINGLE .wav file to perform the downbeat estimation as well as the full output path and filename of the output file. The ability to specify the output path and file name is essential. Denoting the input .wav file path and name as %input and the output file path and name as %output, a program called foobar could be called from the command-line as follows:&lt;br /&gt;
&lt;br /&gt;
 foobar %input %output&lt;br /&gt;
 foobar -i %input -o %output&lt;br /&gt;
&lt;br /&gt;
Moreover, if your submission takes additional parameters, such as a detection threshold, foobar could be called like:&lt;br /&gt;
&lt;br /&gt;
 foobar .1 %input %output&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output  &lt;br /&gt;
&lt;br /&gt;
If your submission is in MATLAB, it should be submitted as a function. Once again, the function must contain String inputs for the full path and names of the input and output files. Parameters could also be specified as input arguments of the function. For example: &lt;br /&gt;
&lt;br /&gt;
 foobar('%input','%output')&lt;br /&gt;
 foobar(.1,'%input','%output')&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== README File ===&lt;br /&gt;
&lt;br /&gt;
A README file accompanying each submission should contain explicit instructions on how to to run the program (as well as contact information, etc.). In particular, each command line to run should be specified, using %input for the input sound file and %output for the resulting text file.&lt;br /&gt;
&lt;br /&gt;
For instance, to test the program foobar with different values for parameters param1, the README file would look like:&lt;br /&gt;
&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output&lt;br /&gt;
 foobar -param1 .15 -i %input -o %output&lt;br /&gt;
 foobar -param1 .2 -i %input -o %output&lt;br /&gt;
 foobar -param1 .25 -i %input -o %output&lt;br /&gt;
 foobar -param1 .3 -i %input -o %output&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
For a submission using MATLAB, the README file could look like:&lt;br /&gt;
&lt;br /&gt;
 matlab -r &amp;quot;foobar(.1,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.15,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.2,'%input','%output');quit;&amp;quot; &lt;br /&gt;
 matlab -r &amp;quot;foobar(.25,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.3,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
The different command lines to evaluate the performance of each parameter set over the whole database will be generated automatically from each line in the README file containing both '%input' and '%output' strings.&lt;br /&gt;
&lt;br /&gt;
== Evaluation Procedures ==&lt;br /&gt;
&lt;br /&gt;
For the evalution procedure we will use&lt;br /&gt;
*'''F-measure''' - the standard calculation as used in onset and beat tracking evaluation with a +/-70ms window, see Dixon (2007).&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Time and hardware limits ==&lt;br /&gt;
Due to the potentially high number of participants in this and other audio tasks, hard limits on the runtime of submissions will be imposed.&lt;br /&gt;
&lt;br /&gt;
A hard limit of 24 hours will be imposed on analysis times. Submissions exceeding this limit may not receive a result.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Potential Participants ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
== Discussion ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
= Bibliography =&lt;br /&gt;
S. Dixon, F. Gouyon and G. Widmer, &amp;quot;Towards Characterisation of Music via Rhythmic Patterns&amp;quot;, In 5th International Conference on Music Information Retrieval (ISMIR 2004), pp 509-516.&lt;br /&gt;
&lt;br /&gt;
S. Dixon, &amp;quot;Evaluation of audio beat tracking system beatroot,&amp;quot; Journal of New Music Research, vol. 36, no. 1, pp. 39-51, 2007.&lt;br /&gt;
&lt;br /&gt;
J. A. Hockman, M. E. P. Davies, I. Fujinaga.&amp;quot;ONE IN THE JUNGLE: Downbeat Detection in Hardcore, Jungle, and Drum and Bass&amp;quot;, In Proc of ISMIR 2012, Porto, Portugal pp. 169-174, 2012.&lt;br /&gt;
&lt;br /&gt;
F. Krebs, S. Boeck, and G. Widmer, &amp;quot;Rhythmic Pattern Modeling for Beat- and Downbeat Tracking in Musical Audio&amp;quot;, In Proceedings of 14th International Society for Music Information Retrieval Conference (ISMIR), Curitiba, Brazil, 2013.&lt;br /&gt;
&lt;br /&gt;
M. Mauch, C. Cannam, M. E. P. Davies, S. Dixon, C. Harte, S. Kolozali and D. Tidhar, &amp;quot;OMRAS2 Metadata Project 2009&amp;quot;, Late-breaking session at the 10th International Conference on Music Information Retrieval, 2009.&lt;/div&gt;</summary>
		<author><name>Matthew Davies</name></author>
		
	</entry>
	<entry>
		<id>https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10043</id>
		<title>2014:Audio Downbeat Estimation</title>
		<link rel="alternate" type="text/html" href="https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10043"/>
		<updated>2014-05-23T14:43:53Z</updated>

		<summary type="html">&lt;p&gt;Matthew Davies: &lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Description ==&lt;br /&gt;
This text has been adapted from the Audio Beat Tracking Wiki page.  Please add your comments and discussion at the bottom of this page.&lt;br /&gt;
&lt;br /&gt;
The aim of the automatic downbeat estimation task is to identify the locations of downbeats in a collection of sound files. While this is similar to the Audio Beat Tracking task, here the aim is to find the first beat of each bar (measure) rather than all beat times. Algorithms are '''not''' required to estimate beat times in addition to downbeats.&lt;br /&gt;
&lt;br /&gt;
Submitted algorithms will be evaluated in terms of their accuracy in finding downbeat locations (only) as annotated by musical experts across several diverse datasets.&lt;br /&gt;
&lt;br /&gt;
== Data ==&lt;br /&gt;
&lt;br /&gt;
=== Collections ===&lt;br /&gt;
'''Ballroom'''&lt;br /&gt;
The ballroom dataset contains eight different dance styles (Cha Cha, Jive, Rumba, Samba, Tango, Viennese Waltz and Waltz). It consists of 697 excerpts of 30s in duration.&lt;br /&gt;
The dataset contains two different meters 3/4 and 4/4 - but all pieces have constant meter. For further information see Dixon et al (2004) and Krebs et al (2013).&lt;br /&gt;
Note, we are using the ground truth annotations from Krebs et al. (2013) available at https://github.com/CPJKU/BallroomAnnotations&lt;br /&gt;
&lt;br /&gt;
'''Isophonics (Beatles only)'''&lt;br /&gt;
The Beatles dataset from the Centre for Digital Music at Queen Mary, University of London (http://www.isophonics.net/), as also used for Audio Chord Estimation in MIREX for many years. Available from http://www.isophonics.net/. &lt;br /&gt;
This dataset contains 179 complete songs, the majority of which are in 4/4.&lt;br /&gt;
For further information see Mauch et al (2009).&lt;br /&gt;
&lt;br /&gt;
'''Turkish Data'''&lt;br /&gt;
80 1 minute excerpts in various time-signatures. &lt;br /&gt;
&lt;br /&gt;
'''Cretan Data'''&lt;br /&gt;
42 full songs in various time-signature.&lt;br /&gt;
&lt;br /&gt;
'''HJDB''' (to be confirmed)&lt;br /&gt;
236 excerpts of Hardcore, Jungle and Drum and Bass music between 30s and 2 minutes in length. All excerpts are in 4/4 and have a constant tempo. &lt;br /&gt;
For further information see Hockman et al (2012).&lt;br /&gt;
&lt;br /&gt;
In total this makes 1234 excerpts (of which 259 are full length songs).&lt;br /&gt;
&lt;br /&gt;
=== Audio Formats ===&lt;br /&gt;
&lt;br /&gt;
The data are monophonic sound files, with the associated onset times and data about the annotation robustness.&lt;br /&gt;
&lt;br /&gt;
* CD-quality (PCM, 16-bit, 44100 Hz) for all except Ballroom (originally lower quality, but resampled to 44100 Hz)&lt;br /&gt;
* single channel (mono)&lt;br /&gt;
&lt;br /&gt;
== Submission Format ==&lt;br /&gt;
Submissions to this task will have to conform to a specified format detailed below. Submissions should be packaged and contain at least two files: The algorithm itself and a README containing contact information and detailing, in full, the use of the algorithm.&lt;br /&gt;
&lt;br /&gt;
=== Input Data ===&lt;br /&gt;
Participating algorithms will have to read audio in the following format:&lt;br /&gt;
&lt;br /&gt;
* Sample rate: 44.1 KHz&lt;br /&gt;
* Sample size: 16 bit&lt;br /&gt;
* Number of channels: 1 (mono)&lt;br /&gt;
* Encoding: WAV &lt;br /&gt;
&lt;br /&gt;
=== Output Data ===&lt;br /&gt;
&lt;br /&gt;
The downbeat estimation algorithms will return downbeat times in an ASCII text file for each input .wav audio file. The specification of this output file is immediately below.&lt;br /&gt;
&lt;br /&gt;
=== Output File Format (Audio Downbeat Estimation) ===&lt;br /&gt;
&lt;br /&gt;
The downbeat output file format is an ASCII text format. Each downbeat time is specified, in seconds, on its own line. Specifically, &lt;br /&gt;
&lt;br /&gt;
 &amp;lt;downbeat time(in seconds)&amp;gt;\n&lt;br /&gt;
&lt;br /&gt;
where \n denotes the end of line. The &amp;lt; and &amp;gt; characters are not included. An example output file would look something like:&lt;br /&gt;
&lt;br /&gt;
 0.243&lt;br /&gt;
 1.486&lt;br /&gt;
 2.729&lt;br /&gt;
&lt;br /&gt;
=== Algorithm Calling Format ===&lt;br /&gt;
&lt;br /&gt;
The submitted algorithm must take as arguments a SINGLE .wav file to perform the downbeat estimation as well as the full output path and filename of the output file. The ability to specify the output path and file name is essential. Denoting the input .wav file path and name as %input and the output file path and name as %output, a program called foobar could be called from the command-line as follows:&lt;br /&gt;
&lt;br /&gt;
 foobar %input %output&lt;br /&gt;
 foobar -i %input -o %output&lt;br /&gt;
&lt;br /&gt;
Moreover, if your submission takes additional parameters, such as a detection threshold, foobar could be called like:&lt;br /&gt;
&lt;br /&gt;
 foobar .1 %input %output&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output  &lt;br /&gt;
&lt;br /&gt;
If your submission is in MATLAB, it should be submitted as a function. Once again, the function must contain String inputs for the full path and names of the input and output files. Parameters could also be specified as input arguments of the function. For example: &lt;br /&gt;
&lt;br /&gt;
 foobar('%input','%output')&lt;br /&gt;
 foobar(.1,'%input','%output')&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== README File ===&lt;br /&gt;
&lt;br /&gt;
A README file accompanying each submission should contain explicit instructions on how to to run the program (as well as contact information, etc.). In particular, each command line to run should be specified, using %input for the input sound file and %output for the resulting text file.&lt;br /&gt;
&lt;br /&gt;
For instance, to test the program foobar with different values for parameters param1, the README file would look like:&lt;br /&gt;
&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output&lt;br /&gt;
 foobar -param1 .15 -i %input -o %output&lt;br /&gt;
 foobar -param1 .2 -i %input -o %output&lt;br /&gt;
 foobar -param1 .25 -i %input -o %output&lt;br /&gt;
 foobar -param1 .3 -i %input -o %output&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
For a submission using MATLAB, the README file could look like:&lt;br /&gt;
&lt;br /&gt;
 matlab -r &amp;quot;foobar(.1,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.15,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.2,'%input','%output');quit;&amp;quot; &lt;br /&gt;
 matlab -r &amp;quot;foobar(.25,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.3,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
The different command lines to evaluate the performance of each parameter set over the whole database will be generated automatically from each line in the README file containing both '%input' and '%output' strings.&lt;br /&gt;
&lt;br /&gt;
== Evaluation Procedures ==&lt;br /&gt;
&lt;br /&gt;
For the evalution procedure we will use&lt;br /&gt;
*'''F-measure''' - the standard calculation as used in onset and beat tracking evaluation with a +/-70ms window, see Dixon (2007).&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Time and hardware limits ==&lt;br /&gt;
Due to the potentially high number of participants in this and other audio tasks, hard limits on the runtime of submissions will be imposed.&lt;br /&gt;
&lt;br /&gt;
A hard limit of 24 hours will be imposed on analysis times. Submissions exceeding this limit may not receive a result.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Potential Participants ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
== Discussion ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
= Bibliography =&lt;br /&gt;
S. Dixon, F. Gouyon and G. Widmer, &amp;quot;Towards Characterisation of Music via Rhythmic Patterns&amp;quot;, In 5th International Conference on Music Information Retrieval (ISMIR 2004), pp 509-516.&lt;br /&gt;
&lt;br /&gt;
S. Dixon, &amp;quot;Evaluation of audio beat tracking system beatroot,&amp;quot; Journal of New Music Research, vol. 36, no. 1, pp. 39-51, 2007.&lt;br /&gt;
&lt;br /&gt;
J. A. Hockman, M. E. P. Davies, I. Fujinaga.&amp;quot;ONE IN THE JUNGLE: Downbeat Detection in Hardcore, Jungle, and Drum and Bass&amp;quot;, In Proc of ISMIR 2012, Porto, Portugal pp. 169-174, 2012.&lt;br /&gt;
&lt;br /&gt;
F. Krebs, S. Boeck, and G. Widmer, &amp;quot;Rhythmic Pattern Modeling for Beat- and Downbeat Tracking in Musical Audio&amp;quot;, In Proceedings of 14th International Society for Music Information Retrieval Conference (ISMIR), Curitiba, Brazil, 2013.&lt;br /&gt;
&lt;br /&gt;
M. Mauch, C. Cannam, M. E. P. Davies, S. Dixon, C. Harte, S. Kolozali and D. Tidhar, &amp;quot;OMRAS2 Metadata Project 2009&amp;quot;, Late-breaking session at the 10th International Conference on Music Information Retrieval, 2009.&lt;/div&gt;</summary>
		<author><name>Matthew Davies</name></author>
		
	</entry>
	<entry>
		<id>https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10042</id>
		<title>2014:Audio Downbeat Estimation</title>
		<link rel="alternate" type="text/html" href="https://music-ir.org/mirex/w/index.php?title=2014:Audio_Downbeat_Estimation&amp;diff=10042"/>
		<updated>2014-05-23T14:40:42Z</updated>

		<summary type="html">&lt;p&gt;Matthew Davies: Created page with &amp;quot;== Description == This text has been adapted from the Audio Beat Tracking Wiki page.  Please add your comments and discussion at the bottom of this page.  The aim of the automati...&amp;quot;&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Description ==&lt;br /&gt;
This text has been adapted from the Audio Beat Tracking Wiki page.  Please add your comments and discussion at the bottom of this page.&lt;br /&gt;
&lt;br /&gt;
The aim of the automatic downbeat estimation task is to identify the locations of downbeats in a collection of sound files. While this is similar to the Audio Beat Tracking task, here the aim is to find the first beat of each bar (measure) rather than all beat times. Algorithms are '''not''' required to estimate beat times in addition to downbeats.&lt;br /&gt;
&lt;br /&gt;
Submitted algorithms will be evaluated in terms of their accuracy in finding downbeat locations (only) as annotated by musical experts across several diverse datasets.&lt;br /&gt;
&lt;br /&gt;
== Data ==&lt;br /&gt;
&lt;br /&gt;
=== Collections ===&lt;br /&gt;
'''Ballroom'''&lt;br /&gt;
The ballroom dataset contains eight different dance styles (Cha Cha, Jive, Rumba, Samba, Tango, Viennese Waltz and Waltz). It consists of 697 excerpts of 30s in duration.&lt;br /&gt;
The dataset contains two different meters 3/4 and 4/4 - but all pieces have constant meter. For further information see Dixon et al (2004) and Krebs et al (2013).&lt;br /&gt;
Note, we are using the ground truth annotations from Krebs et al. (2013) available at https://github.com/CPJKU/BallroomAnnotations&lt;br /&gt;
&lt;br /&gt;
'''Isophonics (Beatles only)'''&lt;br /&gt;
The Beatles dataset from the Centre for Digital Music at Queen Mary, University of London (http://www.isophonics.net/), as also used for Audio Chord Estimation in MIREX for many years. Available from http://www.isophonics.net/. &lt;br /&gt;
This dataset contains 179 complete songs, the majority of which are in 4/4.&lt;br /&gt;
For further information see Mauch et al (2009).&lt;br /&gt;
&lt;br /&gt;
'''Turkish Data'''&lt;br /&gt;
80 1 minute excerpts in various time-signatures. &lt;br /&gt;
&lt;br /&gt;
'''Cretan Data'''&lt;br /&gt;
42 full songs in various time-signature.&lt;br /&gt;
&lt;br /&gt;
'''HJDB'''&lt;br /&gt;
236 excerpts of Hardcore, Jungle and Drum and Bass music between 30s and 2 minutes in length. All excerpts are in 4/4 and have a constant tempo. &lt;br /&gt;
For further information see Hockman et al (2012).&lt;br /&gt;
&lt;br /&gt;
In total this makes 1234 excerpts (of which 259 are full length songs).&lt;br /&gt;
&lt;br /&gt;
=== Audio Formats ===&lt;br /&gt;
&lt;br /&gt;
The data are monophonic sound files, with the associated onset times and data about the annotation robustness.&lt;br /&gt;
&lt;br /&gt;
* CD-quality (PCM, 16-bit, 44100 Hz) for all except Ballroom (originally lower quality, but resampled to 44100 Hz)&lt;br /&gt;
* single channel (mono)&lt;br /&gt;
&lt;br /&gt;
== Submission Format ==&lt;br /&gt;
Submissions to this task will have to conform to a specified format detailed below. Submissions should be packaged and contain at least two files: The algorithm itself and a README containing contact information and detailing, in full, the use of the algorithm.&lt;br /&gt;
&lt;br /&gt;
=== Input Data ===&lt;br /&gt;
Participating algorithms will have to read audio in the following format:&lt;br /&gt;
&lt;br /&gt;
* Sample rate: 44.1 KHz&lt;br /&gt;
* Sample size: 16 bit&lt;br /&gt;
* Number of channels: 1 (mono)&lt;br /&gt;
* Encoding: WAV &lt;br /&gt;
&lt;br /&gt;
=== Output Data ===&lt;br /&gt;
&lt;br /&gt;
The downbeat estimation algorithms will return downbeat times in an ASCII text file for each input .wav audio file. The specification of this output file is immediately below.&lt;br /&gt;
&lt;br /&gt;
=== Output File Format (Audio Downbeat Estimation) ===&lt;br /&gt;
&lt;br /&gt;
The downbeat output file format is an ASCII text format. Each downbeat time is specified, in seconds, on its own line. Specifically, &lt;br /&gt;
&lt;br /&gt;
 &amp;lt;downbeat time(in seconds)&amp;gt;\n&lt;br /&gt;
&lt;br /&gt;
where \n denotes the end of line. The &amp;lt; and &amp;gt; characters are not included. An example output file would look something like:&lt;br /&gt;
&lt;br /&gt;
 0.243&lt;br /&gt;
 1.486&lt;br /&gt;
 2.729&lt;br /&gt;
&lt;br /&gt;
=== Algorithm Calling Format ===&lt;br /&gt;
&lt;br /&gt;
The submitted algorithm must take as arguments a SINGLE .wav file to perform the downbeat estimation as well as the full output path and filename of the output file. The ability to specify the output path and file name is essential. Denoting the input .wav file path and name as %input and the output file path and name as %output, a program called foobar could be called from the command-line as follows:&lt;br /&gt;
&lt;br /&gt;
 foobar %input %output&lt;br /&gt;
 foobar -i %input -o %output&lt;br /&gt;
&lt;br /&gt;
Moreover, if your submission takes additional parameters, such as a detection threshold, foobar could be called like:&lt;br /&gt;
&lt;br /&gt;
 foobar .1 %input %output&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output  &lt;br /&gt;
&lt;br /&gt;
If your submission is in MATLAB, it should be submitted as a function. Once again, the function must contain String inputs for the full path and names of the input and output files. Parameters could also be specified as input arguments of the function. For example: &lt;br /&gt;
&lt;br /&gt;
 foobar('%input','%output')&lt;br /&gt;
 foobar(.1,'%input','%output')&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== README File ===&lt;br /&gt;
&lt;br /&gt;
A README file accompanying each submission should contain explicit instructions on how to to run the program (as well as contact information, etc.). In particular, each command line to run should be specified, using %input for the input sound file and %output for the resulting text file.&lt;br /&gt;
&lt;br /&gt;
For instance, to test the program foobar with different values for parameters param1, the README file would look like:&lt;br /&gt;
&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output&lt;br /&gt;
 foobar -param1 .15 -i %input -o %output&lt;br /&gt;
 foobar -param1 .2 -i %input -o %output&lt;br /&gt;
 foobar -param1 .25 -i %input -o %output&lt;br /&gt;
 foobar -param1 .3 -i %input -o %output&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
For a submission using MATLAB, the README file could look like:&lt;br /&gt;
&lt;br /&gt;
 matlab -r &amp;quot;foobar(.1,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.15,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.2,'%input','%output');quit;&amp;quot; &lt;br /&gt;
 matlab -r &amp;quot;foobar(.25,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.3,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
The different command lines to evaluate the performance of each parameter set over the whole database will be generated automatically from each line in the README file containing both '%input' and '%output' strings.&lt;br /&gt;
&lt;br /&gt;
== Evaluation Procedures ==&lt;br /&gt;
&lt;br /&gt;
For the evalution procedure we will use&lt;br /&gt;
*'''F-measure''' - the standard calculation as used in onset and beat tracking evaluation with a +/-70ms window, see Dixon (2007).&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Time and hardware limits ==&lt;br /&gt;
Due to the potentially high number of participants in this and other audio tasks, hard limits on the runtime of submissions will be imposed.&lt;br /&gt;
&lt;br /&gt;
A hard limit of 24 hours will be imposed on analysis times. Submissions exceeding this limit may not receive a result.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Potential Participants ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
== Discussion ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
= Bibliography =&lt;br /&gt;
S. Dixon, F. Gouyon and G. Widmer, &amp;quot;Towards Characterisation of Music via Rhythmic Patterns&amp;quot;, In 5th International Conference on Music Information Retrieval (ISMIR 2004), pp 509-516.&lt;br /&gt;
&lt;br /&gt;
S. Dixon, &amp;quot;Evaluation of audio beat tracking system beatroot,&amp;quot; Journal of New Music Research, vol. 36, no. 1, pp. 39-51, 2007.&lt;br /&gt;
&lt;br /&gt;
J. A. Hockman, M. E. P. Davies, I. Fujinaga.&amp;quot;ONE IN THE JUNGLE: Downbeat Detection in Hardcore, Jungle, and Drum and Bass&amp;quot;, In Proc of ISMIR 2012, Porto, Portugal pp. 169-174, 2012.&lt;br /&gt;
&lt;br /&gt;
F. Krebs, S. Boeck, and G. Widmer, &amp;quot;Rhythmic Pattern Modeling for Beat- and Downbeat Tracking in Musical Audio&amp;quot;, In Proceedings of 14th International Society for Music Information Retrieval Conference (ISMIR), Curitiba, Brazil, 2013.&lt;br /&gt;
&lt;br /&gt;
M. Mauch, C. Cannam, M. E. P. Davies, S. Dixon, C. Harte, S. Kolozali and D. Tidhar, &amp;quot;OMRAS2 Metadata Project 2009&amp;quot;, Late-breaking session at the 10th International Conference on Music Information Retrieval, 2009.&lt;/div&gt;</summary>
		<author><name>Matthew Davies</name></author>
		
	</entry>
	<entry>
		<id>https://music-ir.org/mirex/w/index.php?title=2012:Audio_Chord_Estimation&amp;diff=8852</id>
		<title>2012:Audio Chord Estimation</title>
		<link rel="alternate" type="text/html" href="https://music-ir.org/mirex/w/index.php?title=2012:Audio_Chord_Estimation&amp;diff=8852"/>
		<updated>2012-08-09T08:32:48Z</updated>

		<summary type="html">&lt;p&gt;Matthew Davies: /* Discussion */&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;[[The Utrecht Agreement on Chord Evaluation]]&lt;br /&gt;
&lt;br /&gt;
===Evaluation of Chord Transcriptions===&lt;br /&gt;
&lt;br /&gt;
Before the final description of the chord evaluation goes live here, please see the discussion based on the [[The Utrecht Agreement on Chord Evaluation]].&lt;br /&gt;
&lt;br /&gt;
== Description ==&lt;br /&gt;
This task requires participants to extract or transcribe a sequence of chords from an audio music recording. For many applications in music information retrieval, extracting the harmonic structure of an audio track is very desirable, for example for segmenting pieces into characteristic segments, for finding similar pieces, or for semantic analysis of music.&lt;br /&gt;
&lt;br /&gt;
The extraction of the harmonic structure requires the detection of as many chords as possible in a piece. That includes the characterisation of chords with a key and type as well as a chronological order with onset and duration of the chords.&lt;br /&gt;
&lt;br /&gt;
Although some publications are available on this topic [1,2,3,4,5], comparison of the results is difficult, because different measures are used to assess the performance. To overcome this problem an accurately defined methodology is needed. This includes a repertory of the findable chords, a defined test set along with ground truth and unambiguous calculation rules to measure the performance.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Data ==&lt;br /&gt;
Two datasets are used to evaluate chord transcription accuracy:&lt;br /&gt;
&lt;br /&gt;
=== Beatles dataset ===&lt;br /&gt;
Christopher Harte`s Beatles dataset consisting of annotations of 12 Beatles albums.&lt;br /&gt;
&lt;br /&gt;
The text annotation procedure of musical chords that was used to produce this dataset is presented in [6]. &lt;br /&gt;
&lt;br /&gt;
=== Queen and Zweieck dataset ===&lt;br /&gt;
Matthias Mauch's Queen and Zweieck dataset consisting of 38 songs from Queen and Zweieck.&lt;br /&gt;
&lt;br /&gt;
===Example ground-truth file ===&lt;br /&gt;
The ground-truth files take the form:&lt;br /&gt;
&lt;br /&gt;
 ...&lt;br /&gt;
 41.2631021 44.2456460 B&lt;br /&gt;
 44.2456460 45.7201230 E&lt;br /&gt;
 45.7201230 47.2061900 E:7/3&lt;br /&gt;
 47.2061900 48.6922670 A&lt;br /&gt;
 48.6922670 50.1551240 A:min/b3&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Evaluation ==&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== Segmentation Score ===&lt;br /&gt;
&lt;br /&gt;
The segmentation score will be calculated using directional hamming distance as described in [8]. An over-segmentation value (m) and an under-segmentation value (f) will be calculated and the final segmentation score will be calculated using the worst case from these two i.e:&lt;br /&gt;
&lt;br /&gt;
segmentation score = 1 - max(m,f)&lt;br /&gt;
&lt;br /&gt;
m and f are not independent of each other so combining them this way ensures that a good score in one does not hide a bad score in the other. The combined segmentation score can take values between 0 and 1 with 0 being the worst and 1 being the best result.-- Chrish 17:05, 9 September 2009 (UTC)&lt;br /&gt;
&lt;br /&gt;
=== Frame-based recall ===&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
For recall evaluation, we may define a different chord dictionary for each level of evaluation (dyads, triads, tetrads etc). Each dictionary is a text file containing chord shorthands / interval lists of the chords that will be considered in that evaluation. The following dictionaries are proposed:&lt;br /&gt;
&lt;br /&gt;
For dyad comparison of major/minor chords only:&lt;br /&gt;
&lt;br /&gt;
N&amp;lt;br&amp;gt;&lt;br /&gt;
X:maj&amp;lt;br&amp;gt;&lt;br /&gt;
X:min&amp;lt;br&amp;gt;&lt;br /&gt;
&lt;br /&gt;
For comparison of standard triad chords:&lt;br /&gt;
&lt;br /&gt;
N&amp;lt;br&amp;gt;&lt;br /&gt;
X:maj&amp;lt;br&amp;gt;&lt;br /&gt;
X:min&amp;lt;br&amp;gt;&lt;br /&gt;
X:aug&amp;lt;br&amp;gt;&lt;br /&gt;
X:dim&amp;lt;br&amp;gt;&lt;br /&gt;
X:sus2&amp;lt;br&amp;gt;&lt;br /&gt;
X:sus4&amp;lt;br&amp;gt;&lt;br /&gt;
&lt;br /&gt;
For comparison of tetrad (quad) chords:&lt;br /&gt;
&lt;br /&gt;
N &amp;lt;br&amp;gt;&lt;br /&gt;
X:maj &amp;lt;br&amp;gt;&lt;br /&gt;
X:min&amp;lt;br&amp;gt;&lt;br /&gt;
X:aug&amp;lt;br&amp;gt;&lt;br /&gt;
X:dim&amp;lt;br&amp;gt;&lt;br /&gt;
X:sus2&amp;lt;br&amp;gt;&lt;br /&gt;
X:sus4&amp;lt;br&amp;gt;&lt;br /&gt;
X:maj7&amp;lt;br&amp;gt;&lt;br /&gt;
X:7&amp;lt;br&amp;gt;&lt;br /&gt;
X:maj(9)&amp;lt;br&amp;gt;&lt;br /&gt;
X:aug(7)	&amp;lt;br&amp;gt;&lt;br /&gt;
X:min(7)&amp;lt;br&amp;gt;&lt;br /&gt;
X:min7&amp;lt;br&amp;gt;&lt;br /&gt;
X:min(9)&amp;lt;br&amp;gt;&lt;br /&gt;
X:dim(7)&amp;lt;br&amp;gt;&lt;br /&gt;
X:hdim7	&amp;lt;br&amp;gt;&lt;br /&gt;
X:sus4(7)&amp;lt;br&amp;gt;&lt;br /&gt;
X:sus4(b7)&amp;lt;br&amp;gt;&lt;br /&gt;
X:dim7&amp;lt;br&amp;gt;&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
For each evaluation level, the ground truth annotation is compared against the dictionary. Any chord label not belonging to the current dictionary will be replaced with an &amp;quot;X&amp;quot; in a local copy of the annotation and will not be included in the recall calculation.&lt;br /&gt;
&lt;br /&gt;
Note that the level of comparison in terms of intervals can be varied. For example, in a triad evaluation we can consider the first three component intervals in the chord so that a major (1,3,5) and a major7 (1,3,5,7) will be considered the same chord. For a tetrad (quad) evaluation, we would consider the first 4 intervals so major and major7 would then be considered to be different chords.&lt;br /&gt;
&lt;br /&gt;
For the maj/min evaluation (using the first example dictionary), using an interval comparison of 2 (dyad) will compare only the first two intervals of each chord label. This would map augmented and diminished chords to major and minor respectively (and any other symbols that had a major 3rd or minor 3rd as their first interval). Using an interval comparison of 3 with the same dictionary would keep only those chords that have major and minor triads as their first 3 intervals so augmented and diminished chords would be removed from the evaluation.&lt;br /&gt;
&lt;br /&gt;
After the annotation has been &amp;quot;filtered&amp;quot; using a given dictionary, it can be compared against the machine generated estimates output by the algorithm under test. The chord sequences described in the annotation and estimate text files are sampled at a given frame rate (in this case 10ms per frame) to give two sequences of chord frames which may be compared directly with each other. For calculating a hit or a miss, the chord labels from the current frame in each sequence will be compared.  Chord comparison is done by converting each chord label into an ordered list of pitch classes then comparing the two lists element by element. If the lists match to the required number of intervals then a hit is recorded, otherwise the estimate is considered a miss. It should be noted that, by converting to pitch classes in the comparison, this evaluation ignores enharmonic pitch and interval spellings so the following chords (slightly silly example just for illustration) will all evaluate as identical:&lt;br /&gt;
&lt;br /&gt;
C:maj = Dbb:maj = C#:(b1,b3,#4)&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
Basic recall calculation algorithm:&lt;br /&gt;
&lt;br /&gt;
1) filter annotated transcription using chord dictionary for a defined number of intervals&lt;br /&gt;
&lt;br /&gt;
2) sample annotated transcription and machine estimated transcription at 10ms intervals to create a sequence of annotation frames and estimate frames&lt;br /&gt;
&lt;br /&gt;
3) start at the first frame&lt;br /&gt;
&lt;br /&gt;
4) get chord label for current annotation frame and estimate frame&lt;br /&gt;
&lt;br /&gt;
5) check annotation label:&amp;lt;br&amp;gt;&lt;br /&gt;
&lt;br /&gt;
IF symbol is 'X' (i.e. non-dictionary) &amp;lt;br&amp;gt;&lt;br /&gt;
&lt;br /&gt;
THEN ignore frame (record number of ignored frames)&amp;lt;br&amp;gt;&lt;br /&gt;
&lt;br /&gt;
ELSE compare annotated/estimated chords for the predefined number of intervals &amp;lt;br&amp;gt;&lt;br /&gt;
increment hit count if chords match&amp;lt;br&amp;gt;&lt;br /&gt;
&lt;br /&gt;
ENDIF&lt;br /&gt;
&lt;br /&gt;
6) increment frame count &lt;br /&gt;
&lt;br /&gt;
7) go back to 4 until final chord frame&lt;br /&gt;
--[[User:Chrish|Chrish]] 17:05, 9 September 2009 (UTC)&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Submission Format ==&lt;br /&gt;
&lt;br /&gt;
=== Audio Format ===&lt;br /&gt;
Audio tracks will be encoded as 44.1 kHz 16bit mono WAV files.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== I/O Format ===&lt;br /&gt;
The expected output chord transcription file for participating algorithms is that proposed by Christopher Harte [6]. &lt;br /&gt;
&lt;br /&gt;
Hence, algorithms should output text files with a similar format to that used in the ground truth transcriptions. That is to say, they should be flat text files with chord segment labels and times arranged thus:&lt;br /&gt;
&lt;br /&gt;
 start_time end_time chord_label&lt;br /&gt;
&lt;br /&gt;
with elements separated by white spaces, times given in seconds, chord labels corresponding to the syntax described in [6] and one chord segment per line. &lt;br /&gt;
&lt;br /&gt;
The chord root is given as a natural (A|B|C|D|E|F|G) followed by optional sharp or flat modifiers (#|b). For the evaluation process we may assume enharmonic equivalence for chord roots. For a given chord type on root X, the chord labels can be given as a list of intervals or as a shorthand notation as shown in the following table:&lt;br /&gt;
&lt;br /&gt;
{|border=&amp;quot;1&amp;quot; cellpadding=&amp;quot;5&amp;quot; cellspacing=&amp;quot;0&amp;quot; align=&amp;quot;center&amp;quot;&lt;br /&gt;
|-&lt;br /&gt;
!NAME&lt;br /&gt;
!INTERVALS&lt;br /&gt;
!SHORTHAND&lt;br /&gt;
|-&lt;br /&gt;
|-*Triads:		&lt;br /&gt;
|-&lt;br /&gt;
|-&lt;br /&gt;
|major&lt;br /&gt;
|X:(1,3,5)&lt;br /&gt;
|X or X:maj &lt;br /&gt;
|-&lt;br /&gt;
|-&lt;br /&gt;
|minor&lt;br /&gt;
|X:(1,b3,5)&lt;br /&gt;
|X:min &lt;br /&gt;
|-&lt;br /&gt;
|-&lt;br /&gt;
|diminished&lt;br /&gt;
|X:(1,b3,b5)&lt;br /&gt;
|X:dim&lt;br /&gt;
|-&lt;br /&gt;
|-&lt;br /&gt;
|augmented&lt;br /&gt;
|X:(1,3,#5)&lt;br /&gt;
|X:aug&lt;br /&gt;
|-&lt;br /&gt;
|-&lt;br /&gt;
|suspended4&lt;br /&gt;
|X:(1,4,5)&lt;br /&gt;
|X:sus4&lt;br /&gt;
|-&lt;br /&gt;
|-&lt;br /&gt;
|possible 6th triad:	&lt;br /&gt;
|&lt;br /&gt;
|	&lt;br /&gt;
|-&lt;br /&gt;
|-&lt;br /&gt;
|suspended2&lt;br /&gt;
|X:(1,2,5)&lt;br /&gt;
|X:sus2&lt;br /&gt;
|-&lt;br /&gt;
|-&lt;br /&gt;
|*Quads: 	&lt;br /&gt;
|&lt;br /&gt;
|	&lt;br /&gt;
|-&lt;br /&gt;
|-&lt;br /&gt;
|major-major7&lt;br /&gt;
|X:(1,3,5,7)&lt;br /&gt;
|X:maj7&lt;br /&gt;
|-&lt;br /&gt;
|-&lt;br /&gt;
|major-minor7&lt;br /&gt;
|X:(1,3,5,b7)&lt;br /&gt;
|X:7&lt;br /&gt;
|-&lt;br /&gt;
|-&lt;br /&gt;
|major-add9&lt;br /&gt;
|X:(1,3,5,9)&lt;br /&gt;
|X:maj(9)&lt;br /&gt;
|-&lt;br /&gt;
|-&lt;br /&gt;
|major-major7-#5&lt;br /&gt;
|X:(1,3,#5,7)&lt;br /&gt;
|X:aug(7)&lt;br /&gt;
|-&lt;br /&gt;
|-&lt;br /&gt;
|minor-major7&lt;br /&gt;
|X:(1,b3,5,7)&lt;br /&gt;
|X:min(7)&lt;br /&gt;
|-&lt;br /&gt;
|-&lt;br /&gt;
|minor-minor7&lt;br /&gt;
|X:(1,b3,5,b7)&lt;br /&gt;
|X:min7&lt;br /&gt;
|-&lt;br /&gt;
|-&lt;br /&gt;
|minor-add9&lt;br /&gt;
|X:(1,b3,5,9)&lt;br /&gt;
|X:min(9)&lt;br /&gt;
|-&lt;br /&gt;
|-&lt;br /&gt;
|minor 7/b5 (ambiguous - could be either of the following)		&lt;br /&gt;
|&lt;br /&gt;
|&lt;br /&gt;
|-&lt;br /&gt;
|-&lt;br /&gt;
|minor-major7-b5&lt;br /&gt;
|X:(1,b3,b5,7)&lt;br /&gt;
|X:dim(7)&lt;br /&gt;
|-&lt;br /&gt;
|-&lt;br /&gt;
|minor-minor7-b5  (a half diminished-7th)&lt;br /&gt;
|X:(1,b3,b5,b7)&lt;br /&gt;
|X:hdim7&lt;br /&gt;
|-&lt;br /&gt;
|-&lt;br /&gt;
|sus4-major7&lt;br /&gt;
|X:(1,4,5,7)&lt;br /&gt;
|X:sus4(7)&lt;br /&gt;
|-&lt;br /&gt;
|-&lt;br /&gt;
|sus4-minor7&lt;br /&gt;
|X:(1,4,5,b7)&lt;br /&gt;
|X:sus4(b7)&lt;br /&gt;
|-&lt;br /&gt;
|-&lt;br /&gt;
|omitted from list on wiki:&lt;br /&gt;
|&lt;br /&gt;
|&lt;br /&gt;
|-&lt;br /&gt;
|-&lt;br /&gt;
|diminished7&lt;br /&gt;
|X:(1,b3,b5,bb7)&lt;br /&gt;
|X:dim7&lt;br /&gt;
|-&lt;br /&gt;
|-&lt;br /&gt;
|No Chord&lt;br /&gt;
|N&lt;br /&gt;
|&lt;br /&gt;
|}&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
Please note that two things have changed in the syntax since it was originally described in [6]. The first change is that the root is no longer implied as a voiced element of a chord so a C major chord (notes C, E and G) should be written C:(1,3,5) instead of just C:(3,5) if using the interval list representation. As before, the labels C and C:maj are equivalent to C:(1,3,5). The second change is that the shorthand label &amp;quot;sus2&amp;quot; (intervals 1,2,5) has been added to the available shorthand list.--[[User:Chrish|Chrish]] 17:05, 9 September 2009 (UTC)&lt;br /&gt;
&lt;br /&gt;
We still accept participants who would only like to be evaluated on major/minor chords and want to use the number format which is an integer chord id on range 0-24, where values 0-11  denote the C major, C# major, ..., B major  and  12-23 denote the C minor, C# minor, ..., B minor and         24    denotes silence or no-chord segments. '''Please note that the format is still the same'''&lt;br /&gt;
&lt;br /&gt;
 start_time end_time chord_number&lt;br /&gt;
&lt;br /&gt;
Systems are supposed to print out the onset-offset times as opposed to MIREX 2008 chord output format where only onset were used.&lt;br /&gt;
&lt;br /&gt;
=== Command line calling format ===&lt;br /&gt;
&lt;br /&gt;
Submissions have to conform to the specified format below:&lt;br /&gt;
&lt;br /&gt;
 ''extractFeaturesAndTrain  &amp;quot;/path/to/trainFileList.txt&amp;quot;  &amp;quot;/path/to/scratch/dir&amp;quot; '' &lt;br /&gt;
&lt;br /&gt;
Where fileList.txt has the paths to each wav file. The features extracted on this stage can be stored under &amp;quot;/path/to/scratch/dir&amp;quot; &lt;br /&gt;
The ground truth files for the supervised learning will be in the same path with a &amp;quot;.txt&amp;quot; extension at the end. For example for &amp;quot;/path/to/trainFile1.wav&amp;quot;, there will be a corresponding ground truth file called &amp;quot;/path/to/trainFile1.wav.txt&amp;quot; . &lt;br /&gt;
&lt;br /&gt;
For testing:&lt;br /&gt;
&lt;br /&gt;
 ''doChordID.sh &amp;quot;/path/to/testFileList.txt&amp;quot;  &amp;quot;/path/to/scratch/dir&amp;quot; &amp;quot;/path/to/results/dir&amp;quot; '' &lt;br /&gt;
&lt;br /&gt;
If there is no training, you can ignore the second argument here. In the results directory, there should be one file for each testfile with same name as the test file + .txt . &lt;br /&gt;
&lt;br /&gt;
Programs can use their working directory if they need to keep temporary cache files or internal debuggin info. Stdout and stderr will be logged.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== Packaging submissions ===&lt;br /&gt;
All submissions should be statically linked to all libraries (the presence of dynamically linked libraries cannot be guaranteed).&lt;br /&gt;
&lt;br /&gt;
All submissions should include a README file including the following information:&lt;br /&gt;
&lt;br /&gt;
* Command line calling format for all executables and an example formatted set of commands&lt;br /&gt;
* Number of threads/cores used or whether this should be specified on the command line&lt;br /&gt;
* Expected memory footprint&lt;br /&gt;
* Expected runtime&lt;br /&gt;
* Any required environments (and versions), e.g. python, java, bash, matlab.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Time and hardware limits ==&lt;br /&gt;
Due to the potentially high number of particpants in this and other audio tasks, hard limits on the runtime of submissions are specified. &lt;br /&gt;
 &lt;br /&gt;
A hard limit of 24 hours will be imposed on runs (total feature extraction and querying times). Submissions that exceed this runtime may not receive a result.&lt;br /&gt;
&lt;br /&gt;
== Discussion ==&lt;br /&gt;
Please write your comments below with your name and date.&lt;br /&gt;
&lt;br /&gt;
Somewhere in the email discussion on the MIREX list, there was a mention that the recent systems run on the Beatles/Queen/Zweieck dataset might have over-learnt the properties of this dataset. I just wondered whether, during or post-MIREX, there was any way to formally/experimentally demonstrate this? I mean, beyond making the observation that there is a &amp;quot;drop&amp;quot; in performance from an open dataset to a closed one. The issue would seem particularly pertinent with regard to this dataset since it's been public for sometime.&lt;br /&gt;
(Matthew Davies, 9th August)&lt;br /&gt;
&lt;br /&gt;
== Potential Participants ==&lt;br /&gt;
name / email&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Bibliography ==&lt;br /&gt;
&lt;br /&gt;
1. Harte,C.A. and Sandler,M.B.(2005). '''Automatic chord identification using a quantised chromagram.''' Proceedings of 118th Audio Engineering Society's Convention.&lt;br /&gt;
&lt;br /&gt;
2. Sailer,C. and Rosenbauer K.(2006). '''A bottom-up approach to chord detection.''' Proceedings of International Computer Music Conference 2006.&lt;br /&gt;
&lt;br /&gt;
3. Shenoy,A. and Wang,Y.(2005). '''Key, chord, and rythm tracking of popular music recordings.''' Computer Music Journal 29(3), 75-86.&lt;br /&gt;
&lt;br /&gt;
4. Sheh,A. and Ellis,D.P.W.(2003). '''Chord segmentation and recognition using em-trained hidden markov models.''' Proceedings of 4th International Conference on Music Information Retrieval.&lt;br /&gt;
&lt;br /&gt;
5. Yoshioka,T. et al.(2004). '''Automatic Chord Transcription with concurrent recognition of chord symbols and boundaries.''' Proceedings of 5th International Conference on Music Information Retrieval.&lt;br /&gt;
&lt;br /&gt;
6. Harte,C. and Sandler,M. and Abdallah,S. and G├│mez,E.(2005). '''Symbolic representation of musical chords: a proposed syntax for text annotations.''' Proceedings of 6th International Conference on Music Information Retrieval.&lt;br /&gt;
&lt;br /&gt;
7. Papadopoulos,H. and Peeters,G.(2007). '''Large-scale study of chord estimation algorithms based on chroma representation and HMM.''' Proceedings of 5th International Conference on Content-Based Multimedia Indexing.&lt;br /&gt;
&lt;br /&gt;
8. Samer Abdallah, Katy Noland, Mark Sandler, Michael Casey &amp;amp; Christophe Rhodes: '''Theory and Evaluation of a Bayesian Music Structure Extractor''' (pp. 420-425) Proc. 6th International Conference on Music Information Retrieval, ISMIR 2005.&lt;/div&gt;</summary>
		<author><name>Matthew Davies</name></author>
		
	</entry>
	<entry>
		<id>https://music-ir.org/mirex/w/index.php?title=2012:Audio_Beat_Tracking&amp;diff=8850</id>
		<title>2012:Audio Beat Tracking</title>
		<link rel="alternate" type="text/html" href="https://music-ir.org/mirex/w/index.php?title=2012:Audio_Beat_Tracking&amp;diff=8850"/>
		<updated>2012-08-03T14:45:18Z</updated>

		<summary type="html">&lt;p&gt;Matthew Davies: /* Collections */&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Description ==&lt;br /&gt;
&lt;br /&gt;
The aim of the automatic beat tracking task is to track each beat locations in a collection of sound files. Unlike the Audio Tempo Extraction task, which aim is to detect tempi for each file, the beat tracking task aims at detecting all beat locations in recordings. The algorithms will be evaluated in terms of their accuracy in predicting beat locations annotated by a group of listeners. &lt;br /&gt;
&lt;br /&gt;
== Data ==&lt;br /&gt;
=== Collections ===&lt;br /&gt;
The original 2006 dataset contains 160 30-second excerpts (WAV format) used for the Audio Tempo and Beat contests in 2006.  Beat locations have been annotated in each excerpt by 40 different listeners (39 listeners for a few excerpts. The length of each excerpt is 30 seconds. These audio recordings were selected to provide a stable tempo value, a wide distribution of tempi values, and a large variety of instrumentation and musical styles. About 20% of the files contain non-binary meters, and a small number of examples contain changing meters.  One disadvantage of using this set for beat tracking is that the tempi are rather stable and this set will not test beat-tracking algorithms in their ability to track tempo changes.&lt;br /&gt;
&lt;br /&gt;
The second collection is comprised of 367 Chopin Mazurkas, represented as full audio tracks (WAV format). The Mazurka dataset contains tempo changes so it will evaluate the ability of algorithms to track these.&lt;br /&gt;
&lt;br /&gt;
The third collection was assembled and donated in 2012. This dataset contains 217 excerpts around 40s each, of which 19 are &amp;quot;easy&amp;quot; and the remaining 198 are &amp;quot;hard&amp;quot;. The harder excerpts were drawn from the following musical styles: Romantic music, ﬁlm soundtracks, blues, chanson and solo guitar. &lt;br /&gt;
&lt;br /&gt;
This dataset has been designed for radically new techniques which can contend with challenging beat tracking situations like: quiet accompaniment, expressive timing, changes in time signature, slow tempo, poor sound quality etc. So, if your beat tracker likes a 4/4 time-signature with a steady tempo and needs clear percussive onsets, don't expect it to do very well!&lt;br /&gt;
But don't be deterred, this is for the good of beat tracking. &lt;br /&gt;
&lt;br /&gt;
You can read in detail about how the dataset was made here:&lt;br /&gt;
[http://dx.doi.org/10.1109/TASL.2012.2205244 ''Selective Sampling for Beat Tracking Evaluation'']&lt;br /&gt;
&lt;br /&gt;
=== Audio Formats ===&lt;br /&gt;
&lt;br /&gt;
The data are monophonic sound files, with the associated onset times and data about the annotation robustness.&lt;br /&gt;
&lt;br /&gt;
* CD-quality (PCM, 16-bit, 44100 Hz)&lt;br /&gt;
* single channel (mono)&lt;br /&gt;
* file length between 2 and 36 seconds (total time: 14 minutes) &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Submission Format ==&lt;br /&gt;
Submissions to this task will have to conform to a specified format detailed below. Submissions should be packaged and contain at least two files: The algorithm itself and a README containing contact information and detailing, in full, the use of the algorithm.&lt;br /&gt;
&lt;br /&gt;
=== Input Data ===&lt;br /&gt;
Participating algorithms will have to read audio in the following format:&lt;br /&gt;
&lt;br /&gt;
* Sample rate: 44.1 KHz&lt;br /&gt;
* Sample size: 16 bit&lt;br /&gt;
* Number of channels: 1 (mono)&lt;br /&gt;
* Encoding: WAV &lt;br /&gt;
&lt;br /&gt;
=== Output Data ===&lt;br /&gt;
&lt;br /&gt;
The beat tracking algorithms will return beat-times in an ASCII text file for each input .wav audio file. The specification of this output file is immediately below.&lt;br /&gt;
&lt;br /&gt;
=== Output File Format (Audio Beat tracking) ===&lt;br /&gt;
&lt;br /&gt;
The Beat Tracking output file format is an ASCII text format. Each beat time is specified, in seconds, on its own line. Specifically, &lt;br /&gt;
&lt;br /&gt;
 &amp;lt;beat time(in seconds)&amp;gt;\n&lt;br /&gt;
&lt;br /&gt;
where \n denotes the end of line. The &amp;lt; and &amp;gt; characters are not included. An example output file would look something like:&lt;br /&gt;
&lt;br /&gt;
 0.243&lt;br /&gt;
 0.486&lt;br /&gt;
 0.729&lt;br /&gt;
&lt;br /&gt;
=== Algorithm Calling Format ===&lt;br /&gt;
&lt;br /&gt;
The submitted algorithm must take as arguments a SINGLE .wav file to perform the onset detection on as well as the full output path and filename of the output file. The ability to specify the output path and file name is essential. Denoting the input .wav file path and name as %input and the output file path and name as %output, a program called foobar could be called from the command-line as follows:&lt;br /&gt;
&lt;br /&gt;
 foobar %input %output&lt;br /&gt;
 foobar -i %input -o %output&lt;br /&gt;
&lt;br /&gt;
Moreover, if your submission takes additional parameters, such as a detection threshold, foobar could be called like:&lt;br /&gt;
&lt;br /&gt;
 foobar .1 %input %output&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output  &lt;br /&gt;
&lt;br /&gt;
If your submission is in MATLAB, it should be submitted as a function. Once again, the function must contain String inputs for the full path and names of the input and output files. Parameters could also be specified as input arguments of the function. For example: &lt;br /&gt;
&lt;br /&gt;
 foobar('%input','%output')&lt;br /&gt;
 foobar(.1,'%input','%output')&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== README File ===&lt;br /&gt;
&lt;br /&gt;
A README file accompanying each submission should contain explicit instructions on how to to run the program (as well as contact information, etc.). In particular, each command line to run should be specified, using %input for the input sound file and %output for the resulting text file.&lt;br /&gt;
&lt;br /&gt;
For instance, to test the program foobar with different values for parameters param1, the README file would look like:&lt;br /&gt;
&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output&lt;br /&gt;
 foobar -param1 .15 -i %input -o %output&lt;br /&gt;
 foobar -param1 .2 -i %input -o %output&lt;br /&gt;
 foobar -param1 .25 -i %input -o %output&lt;br /&gt;
 foobar -param1 .3 -i %input -o %output&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
For a submission using MATLAB, the README file could look like:&lt;br /&gt;
&lt;br /&gt;
 matlab -r &amp;quot;foobar(.1,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.15,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.2,'%input','%output');quit;&amp;quot; &lt;br /&gt;
 matlab -r &amp;quot;foobar(.25,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.3,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
The different command lines to evaluate the performance of each parameter set over the whole database will be generated automatically from each line in the README file containing both '%input' and '%output' strings.&lt;br /&gt;
&lt;br /&gt;
== Evaluation Procedures ==&lt;br /&gt;
&lt;br /&gt;
The evaluation methods are taken from the beat evaluation toolbox and&lt;br /&gt;
are described in the following technical report: &lt;br /&gt;
&lt;br /&gt;
 M. E. P. Davies, N. Degara and M. D. Plumbley. &amp;quot;Evaluation methods for musical audio beat tracking algorithms&amp;quot;. [http://www.elec.qmul.ac.uk/people/markp/2009/DaviesDegaraPlumbley09-evaluation-tr.pdf ''Technical Report C4DM-TR-09-06'']. This link now works! :)&lt;br /&gt;
&lt;br /&gt;
For further details on the specifics of the methods please refer to the&lt;br /&gt;
paper. However, here is a brief summary with appropriate references:&lt;br /&gt;
&lt;br /&gt;
*'''F-measure''' - the standard calculation as used in onset evaluation but&lt;br /&gt;
with a 70ms window. &lt;br /&gt;
&lt;br /&gt;
 S. Dixon, &amp;quot;Onset detection revisited,&amp;quot; in ''Proceedings of 9th&lt;br /&gt;
 International Conference on Digital Audio Effects (DAFx)'', Montreal,&lt;br /&gt;
 Canada, pp. 133-137, 2006.&lt;br /&gt;
&lt;br /&gt;
 S. Dixon, &amp;quot;Evaluation of audio beat tracking system beatroot,&amp;quot; ''Journal&lt;br /&gt;
 of New Music Research'', vol. 36, no. 1, pp. 39-51, 2007.&lt;br /&gt;
&lt;br /&gt;
*'''Cemgil''' - beat accuracy is calculated using a Gaussian error function&lt;br /&gt;
with 40ms standard deviation.&lt;br /&gt;
&lt;br /&gt;
 A. T. Cemgil, B. Kappen, P. Desain, and H. Honing, &amp;quot;On tempo tracking:&lt;br /&gt;
 Tempogram representation and Kalman filtering,&amp;quot; ''Journal Of New Music&lt;br /&gt;
 Research'', vol. 28, no. 4, pp. 259-273, 2001&lt;br /&gt;
 &lt;br /&gt;
*'''Goto''' - binary decision of correct or incorrect tracking based on&lt;br /&gt;
statistical properties of a beat error sequence.&lt;br /&gt;
&lt;br /&gt;
 M. Goto and Y. Muraoka, &amp;quot;Issues in evaluating beat tracking systems,&amp;quot; in&lt;br /&gt;
 ''Working Notes of the IJCAI-97 Workshop on Issues in AI and Music -&lt;br /&gt;
 Evaluation and Assessment'', 1997, pp. 9-16.&lt;br /&gt;
&lt;br /&gt;
*'''PScore''' - McKinney's impulse train cross-correlation method as used in&lt;br /&gt;
2006.&lt;br /&gt;
&lt;br /&gt;
 M. F. McKinney, D. Moelants, M. E. P. Davies, and A. Klapuri,&lt;br /&gt;
 &amp;quot;Evaluation of audio beat tracking and music tempo extraction&lt;br /&gt;
 algorithms,&amp;quot; ''Journal of New Music Research'', vol. 36, no. 1, pp. 1-16,&lt;br /&gt;
 2007.&lt;br /&gt;
&lt;br /&gt;
*'''CMLc''', '''CMLt''', '''AMLc''', '''AMLt''' - continuity-based evaluation methods based on&lt;br /&gt;
the longest continuously correctly tracked section. &lt;br /&gt;
&lt;br /&gt;
 S. Hainsworth, &amp;quot;Techniques for the automated analysis of musical audio,&amp;quot;&lt;br /&gt;
 Ph.D. dissertation, Department of Engineering, Cambridge University,&lt;br /&gt;
 2004.&lt;br /&gt;
&lt;br /&gt;
 A. P. Klapuri, A. Eronen, and J. Astola, &amp;quot;Analysis of the meter of&lt;br /&gt;
 acoustic musical signals,&amp;quot; IEEE Transactions on Audio, Speech and&lt;br /&gt;
 Language Processing, vol. 14, no. 1, pp. 342-355, 2006.&lt;br /&gt;
&lt;br /&gt;
*'''D''', '''Dg''' - information based criteria based on analysis of a beat error&lt;br /&gt;
histogram (note the results are measured in 'bits' and not percentages),&lt;br /&gt;
see the technical report for a description.&lt;br /&gt;
&lt;br /&gt;
== Relevant Development Collections ==&lt;br /&gt;
You can find it here:&lt;br /&gt;
&lt;br /&gt;
https://www.music-ir.org/evaluation/MIREX/data/2006/beat/&lt;br /&gt;
&lt;br /&gt;
User: beattrack Password: b34trx&lt;br /&gt;
&lt;br /&gt;
https://www.music-ir.org/evaluation/MIREX/data/2006/tempo/&lt;br /&gt;
&lt;br /&gt;
User: tempo Password: t3mp0&lt;br /&gt;
&lt;br /&gt;
Data has been uploaded in both .tgz and .zip format.&lt;br /&gt;
&lt;br /&gt;
== Time and hardware limits ==&lt;br /&gt;
Due to the potentially high number of participants in this and other audio tasks, hard limits on the runtime of submissions will be imposed.&lt;br /&gt;
&lt;br /&gt;
A hard limit of 12 hours will be imposed on analysis times. Submissions exceeding this limit may not receive a result.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Submission opening date ==&lt;br /&gt;
&lt;br /&gt;
Friday August 5th 2012&lt;br /&gt;
&lt;br /&gt;
== Submission closing date ==&lt;br /&gt;
Friday September 2nd 2012&lt;br /&gt;
&lt;br /&gt;
== Potential Participants ==&lt;br /&gt;
name / email&lt;/div&gt;</summary>
		<author><name>Matthew Davies</name></author>
		
	</entry>
	<entry>
		<id>https://music-ir.org/mirex/w/index.php?title=2012:Audio_Beat_Tracking&amp;diff=8849</id>
		<title>2012:Audio Beat Tracking</title>
		<link rel="alternate" type="text/html" href="https://music-ir.org/mirex/w/index.php?title=2012:Audio_Beat_Tracking&amp;diff=8849"/>
		<updated>2012-08-03T14:39:27Z</updated>

		<summary type="html">&lt;p&gt;Matthew Davies: /* Collections */&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Description ==&lt;br /&gt;
&lt;br /&gt;
The aim of the automatic beat tracking task is to track each beat locations in a collection of sound files. Unlike the Audio Tempo Extraction task, which aim is to detect tempi for each file, the beat tracking task aims at detecting all beat locations in recordings. The algorithms will be evaluated in terms of their accuracy in predicting beat locations annotated by a group of listeners. &lt;br /&gt;
&lt;br /&gt;
== Data ==&lt;br /&gt;
=== Collections ===&lt;br /&gt;
The original 2006 dataset contains 160 30-second excerpts (WAV format) used for the Audio Tempo and Beat contests in 2006.  Beat locations have been annotated in each excerpt by 40 different listeners (39 listeners for a few excerpts. The length of each excerpt is 30 seconds. These audio recordings were selected to provide a stable tempo value, a wide distribution of tempi values, and a large variety of instrumentation and musical styles. About 20% of the files contain non-binary meters, and a small number of examples contain changing meters.  One disadvantage of using this set for beat tracking is that the tempi are rather stable and this set will not test beat-tracking algorithms in their ability to track tempo changes.&lt;br /&gt;
&lt;br /&gt;
The second collection is comprised of 367 Chopin Mazurkas, represented as full audio tracks (WAV format). The Mazurka dataset contains tempo changes so it will evaluate the ability of algorithms to track these.&lt;br /&gt;
&lt;br /&gt;
The third collection was assembled and donated in 2012. This dataset contains 217 excerpts around 40s each, of which 19 are &amp;quot;easy&amp;quot; and the remaining 198 are &amp;quot;hard&amp;quot;. The harder excerpts were drawn from the following musical styles: Romantic music, ﬁlm soundtracks, blues, chanson and solo guitar. &lt;br /&gt;
&lt;br /&gt;
This dataset has been designed for radically new techniques which can contend with challenging beat tracking situations like: quiet accompaniment, expressive timing, changes in time signature, slow tempo, poor sound quality etc. So, if your beat tracker likes a 4/4 time-signature with a steady tempo and needs clear percussive onsets, don't expect it to do very well!&lt;br /&gt;
But don't be deterred, this is for the good of beat tracking. &lt;br /&gt;
&lt;br /&gt;
You can read in detail about how the dataset was made are here:&lt;br /&gt;
[http://dx.doi.org/10.1109/TASL.2012.2205244 ''Selective Sampling for Beat Tracking Evaluation'']&lt;br /&gt;
&lt;br /&gt;
=== Audio Formats ===&lt;br /&gt;
&lt;br /&gt;
The data are monophonic sound files, with the associated onset times and data about the annotation robustness.&lt;br /&gt;
&lt;br /&gt;
* CD-quality (PCM, 16-bit, 44100 Hz)&lt;br /&gt;
* single channel (mono)&lt;br /&gt;
* file length between 2 and 36 seconds (total time: 14 minutes) &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Submission Format ==&lt;br /&gt;
Submissions to this task will have to conform to a specified format detailed below. Submissions should be packaged and contain at least two files: The algorithm itself and a README containing contact information and detailing, in full, the use of the algorithm.&lt;br /&gt;
&lt;br /&gt;
=== Input Data ===&lt;br /&gt;
Participating algorithms will have to read audio in the following format:&lt;br /&gt;
&lt;br /&gt;
* Sample rate: 44.1 KHz&lt;br /&gt;
* Sample size: 16 bit&lt;br /&gt;
* Number of channels: 1 (mono)&lt;br /&gt;
* Encoding: WAV &lt;br /&gt;
&lt;br /&gt;
=== Output Data ===&lt;br /&gt;
&lt;br /&gt;
The beat tracking algorithms will return beat-times in an ASCII text file for each input .wav audio file. The specification of this output file is immediately below.&lt;br /&gt;
&lt;br /&gt;
=== Output File Format (Audio Beat tracking) ===&lt;br /&gt;
&lt;br /&gt;
The Beat Tracking output file format is an ASCII text format. Each beat time is specified, in seconds, on its own line. Specifically, &lt;br /&gt;
&lt;br /&gt;
 &amp;lt;beat time(in seconds)&amp;gt;\n&lt;br /&gt;
&lt;br /&gt;
where \n denotes the end of line. The &amp;lt; and &amp;gt; characters are not included. An example output file would look something like:&lt;br /&gt;
&lt;br /&gt;
 0.243&lt;br /&gt;
 0.486&lt;br /&gt;
 0.729&lt;br /&gt;
&lt;br /&gt;
=== Algorithm Calling Format ===&lt;br /&gt;
&lt;br /&gt;
The submitted algorithm must take as arguments a SINGLE .wav file to perform the onset detection on as well as the full output path and filename of the output file. The ability to specify the output path and file name is essential. Denoting the input .wav file path and name as %input and the output file path and name as %output, a program called foobar could be called from the command-line as follows:&lt;br /&gt;
&lt;br /&gt;
 foobar %input %output&lt;br /&gt;
 foobar -i %input -o %output&lt;br /&gt;
&lt;br /&gt;
Moreover, if your submission takes additional parameters, such as a detection threshold, foobar could be called like:&lt;br /&gt;
&lt;br /&gt;
 foobar .1 %input %output&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output  &lt;br /&gt;
&lt;br /&gt;
If your submission is in MATLAB, it should be submitted as a function. Once again, the function must contain String inputs for the full path and names of the input and output files. Parameters could also be specified as input arguments of the function. For example: &lt;br /&gt;
&lt;br /&gt;
 foobar('%input','%output')&lt;br /&gt;
 foobar(.1,'%input','%output')&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== README File ===&lt;br /&gt;
&lt;br /&gt;
A README file accompanying each submission should contain explicit instructions on how to to run the program (as well as contact information, etc.). In particular, each command line to run should be specified, using %input for the input sound file and %output for the resulting text file.&lt;br /&gt;
&lt;br /&gt;
For instance, to test the program foobar with different values for parameters param1, the README file would look like:&lt;br /&gt;
&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output&lt;br /&gt;
 foobar -param1 .15 -i %input -o %output&lt;br /&gt;
 foobar -param1 .2 -i %input -o %output&lt;br /&gt;
 foobar -param1 .25 -i %input -o %output&lt;br /&gt;
 foobar -param1 .3 -i %input -o %output&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
For a submission using MATLAB, the README file could look like:&lt;br /&gt;
&lt;br /&gt;
 matlab -r &amp;quot;foobar(.1,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.15,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.2,'%input','%output');quit;&amp;quot; &lt;br /&gt;
 matlab -r &amp;quot;foobar(.25,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.3,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
The different command lines to evaluate the performance of each parameter set over the whole database will be generated automatically from each line in the README file containing both '%input' and '%output' strings.&lt;br /&gt;
&lt;br /&gt;
== Evaluation Procedures ==&lt;br /&gt;
&lt;br /&gt;
The evaluation methods are taken from the beat evaluation toolbox and&lt;br /&gt;
are described in the following technical report: &lt;br /&gt;
&lt;br /&gt;
 M. E. P. Davies, N. Degara and M. D. Plumbley. &amp;quot;Evaluation methods for musical audio beat tracking algorithms&amp;quot;. [http://www.elec.qmul.ac.uk/people/markp/2009/DaviesDegaraPlumbley09-evaluation-tr.pdf ''Technical Report C4DM-TR-09-06'']. This link now works! :)&lt;br /&gt;
&lt;br /&gt;
For further details on the specifics of the methods please refer to the&lt;br /&gt;
paper. However, here is a brief summary with appropriate references:&lt;br /&gt;
&lt;br /&gt;
*'''F-measure''' - the standard calculation as used in onset evaluation but&lt;br /&gt;
with a 70ms window. &lt;br /&gt;
&lt;br /&gt;
 S. Dixon, &amp;quot;Onset detection revisited,&amp;quot; in ''Proceedings of 9th&lt;br /&gt;
 International Conference on Digital Audio Effects (DAFx)'', Montreal,&lt;br /&gt;
 Canada, pp. 133-137, 2006.&lt;br /&gt;
&lt;br /&gt;
 S. Dixon, &amp;quot;Evaluation of audio beat tracking system beatroot,&amp;quot; ''Journal&lt;br /&gt;
 of New Music Research'', vol. 36, no. 1, pp. 39-51, 2007.&lt;br /&gt;
&lt;br /&gt;
*'''Cemgil''' - beat accuracy is calculated using a Gaussian error function&lt;br /&gt;
with 40ms standard deviation.&lt;br /&gt;
&lt;br /&gt;
 A. T. Cemgil, B. Kappen, P. Desain, and H. Honing, &amp;quot;On tempo tracking:&lt;br /&gt;
 Tempogram representation and Kalman filtering,&amp;quot; ''Journal Of New Music&lt;br /&gt;
 Research'', vol. 28, no. 4, pp. 259-273, 2001&lt;br /&gt;
 &lt;br /&gt;
*'''Goto''' - binary decision of correct or incorrect tracking based on&lt;br /&gt;
statistical properties of a beat error sequence.&lt;br /&gt;
&lt;br /&gt;
 M. Goto and Y. Muraoka, &amp;quot;Issues in evaluating beat tracking systems,&amp;quot; in&lt;br /&gt;
 ''Working Notes of the IJCAI-97 Workshop on Issues in AI and Music -&lt;br /&gt;
 Evaluation and Assessment'', 1997, pp. 9-16.&lt;br /&gt;
&lt;br /&gt;
*'''PScore''' - McKinney's impulse train cross-correlation method as used in&lt;br /&gt;
2006.&lt;br /&gt;
&lt;br /&gt;
 M. F. McKinney, D. Moelants, M. E. P. Davies, and A. Klapuri,&lt;br /&gt;
 &amp;quot;Evaluation of audio beat tracking and music tempo extraction&lt;br /&gt;
 algorithms,&amp;quot; ''Journal of New Music Research'', vol. 36, no. 1, pp. 1-16,&lt;br /&gt;
 2007.&lt;br /&gt;
&lt;br /&gt;
*'''CMLc''', '''CMLt''', '''AMLc''', '''AMLt''' - continuity-based evaluation methods based on&lt;br /&gt;
the longest continuously correctly tracked section. &lt;br /&gt;
&lt;br /&gt;
 S. Hainsworth, &amp;quot;Techniques for the automated analysis of musical audio,&amp;quot;&lt;br /&gt;
 Ph.D. dissertation, Department of Engineering, Cambridge University,&lt;br /&gt;
 2004.&lt;br /&gt;
&lt;br /&gt;
 A. P. Klapuri, A. Eronen, and J. Astola, &amp;quot;Analysis of the meter of&lt;br /&gt;
 acoustic musical signals,&amp;quot; IEEE Transactions on Audio, Speech and&lt;br /&gt;
 Language Processing, vol. 14, no. 1, pp. 342-355, 2006.&lt;br /&gt;
&lt;br /&gt;
*'''D''', '''Dg''' - information based criteria based on analysis of a beat error&lt;br /&gt;
histogram (note the results are measured in 'bits' and not percentages),&lt;br /&gt;
see the technical report for a description.&lt;br /&gt;
&lt;br /&gt;
== Relevant Development Collections ==&lt;br /&gt;
You can find it here:&lt;br /&gt;
&lt;br /&gt;
https://www.music-ir.org/evaluation/MIREX/data/2006/beat/&lt;br /&gt;
&lt;br /&gt;
User: beattrack Password: b34trx&lt;br /&gt;
&lt;br /&gt;
https://www.music-ir.org/evaluation/MIREX/data/2006/tempo/&lt;br /&gt;
&lt;br /&gt;
User: tempo Password: t3mp0&lt;br /&gt;
&lt;br /&gt;
Data has been uploaded in both .tgz and .zip format.&lt;br /&gt;
&lt;br /&gt;
== Time and hardware limits ==&lt;br /&gt;
Due to the potentially high number of participants in this and other audio tasks, hard limits on the runtime of submissions will be imposed.&lt;br /&gt;
&lt;br /&gt;
A hard limit of 12 hours will be imposed on analysis times. Submissions exceeding this limit may not receive a result.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Submission opening date ==&lt;br /&gt;
&lt;br /&gt;
Friday August 5th 2012&lt;br /&gt;
&lt;br /&gt;
== Submission closing date ==&lt;br /&gt;
Friday September 2nd 2012&lt;br /&gt;
&lt;br /&gt;
== Potential Participants ==&lt;br /&gt;
name / email&lt;/div&gt;</summary>
		<author><name>Matthew Davies</name></author>
		
	</entry>
	<entry>
		<id>https://music-ir.org/mirex/w/index.php?title=2012:Audio_Beat_Tracking&amp;diff=8848</id>
		<title>2012:Audio Beat Tracking</title>
		<link rel="alternate" type="text/html" href="https://music-ir.org/mirex/w/index.php?title=2012:Audio_Beat_Tracking&amp;diff=8848"/>
		<updated>2012-08-03T14:17:50Z</updated>

		<summary type="html">&lt;p&gt;Matthew Davies: /* Evaluation Procedures */&lt;/p&gt;
&lt;hr /&gt;
&lt;div&gt;== Description ==&lt;br /&gt;
&lt;br /&gt;
The aim of the automatic beat tracking task is to track each beat locations in a collection of sound files. Unlike the Audio Tempo Extraction task, which aim is to detect tempi for each file, the beat tracking task aims at detecting all beat locations in recordings. The algorithms will be evaluated in terms of their accuracy in predicting beat locations annotated by a group of listeners. &lt;br /&gt;
&lt;br /&gt;
== Data ==&lt;br /&gt;
=== Collections ===&lt;br /&gt;
The original 2006 dataset contains 160 30-second excerpts (WAV format) used for the Audio Tempo and Beat contests in 2006.  Beat locations have been annotated in each excerpt by 40 different listeners (39 listeners for a few excerpts. The length of each excerpt is 30 seconds. These audio recordings were selected to provide a stable tempo value, a wide distribution of tempi values, and a large variety of instrumentation and musical styles. About 20% of the files contain non-binary meters, and a small number of examples contain changing meters.  One disadvantage of using this set for beat tracking is that the tempi are rather stable and this set will not test beat-tracking algorithms in their ability to track tempo changes.&lt;br /&gt;
&lt;br /&gt;
The second collection is comprised of 367 Chopin Mazurkas, represented as full audio tracks (WAV format). The Mazurka dataset contains tempo changes so it will evaluate the ability of algorithms to track these.&lt;br /&gt;
&lt;br /&gt;
The third collection was assembled and donated in 2012, and consists of what should be fairly challenging pieces for beat trackers.&lt;br /&gt;
&lt;br /&gt;
=== Audio Formats ===&lt;br /&gt;
&lt;br /&gt;
The data are monophonic sound files, with the associated onset times and data about the annotation robustness.&lt;br /&gt;
&lt;br /&gt;
* CD-quality (PCM, 16-bit, 44100 Hz)&lt;br /&gt;
* single channel (mono)&lt;br /&gt;
* file length between 2 and 36 seconds (total time: 14 minutes) &lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Submission Format ==&lt;br /&gt;
Submissions to this task will have to conform to a specified format detailed below. Submissions should be packaged and contain at least two files: The algorithm itself and a README containing contact information and detailing, in full, the use of the algorithm.&lt;br /&gt;
&lt;br /&gt;
=== Input Data ===&lt;br /&gt;
Participating algorithms will have to read audio in the following format:&lt;br /&gt;
&lt;br /&gt;
* Sample rate: 44.1 KHz&lt;br /&gt;
* Sample size: 16 bit&lt;br /&gt;
* Number of channels: 1 (mono)&lt;br /&gt;
* Encoding: WAV &lt;br /&gt;
&lt;br /&gt;
=== Output Data ===&lt;br /&gt;
&lt;br /&gt;
The beat tracking algorithms will return beat-times in an ASCII text file for each input .wav audio file. The specification of this output file is immediately below.&lt;br /&gt;
&lt;br /&gt;
=== Output File Format (Audio Beat tracking) ===&lt;br /&gt;
&lt;br /&gt;
The Beat Tracking output file format is an ASCII text format. Each beat time is specified, in seconds, on its own line. Specifically, &lt;br /&gt;
&lt;br /&gt;
 &amp;lt;beat time(in seconds)&amp;gt;\n&lt;br /&gt;
&lt;br /&gt;
where \n denotes the end of line. The &amp;lt; and &amp;gt; characters are not included. An example output file would look something like:&lt;br /&gt;
&lt;br /&gt;
 0.243&lt;br /&gt;
 0.486&lt;br /&gt;
 0.729&lt;br /&gt;
&lt;br /&gt;
=== Algorithm Calling Format ===&lt;br /&gt;
&lt;br /&gt;
The submitted algorithm must take as arguments a SINGLE .wav file to perform the onset detection on as well as the full output path and filename of the output file. The ability to specify the output path and file name is essential. Denoting the input .wav file path and name as %input and the output file path and name as %output, a program called foobar could be called from the command-line as follows:&lt;br /&gt;
&lt;br /&gt;
 foobar %input %output&lt;br /&gt;
 foobar -i %input -o %output&lt;br /&gt;
&lt;br /&gt;
Moreover, if your submission takes additional parameters, such as a detection threshold, foobar could be called like:&lt;br /&gt;
&lt;br /&gt;
 foobar .1 %input %output&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output  &lt;br /&gt;
&lt;br /&gt;
If your submission is in MATLAB, it should be submitted as a function. Once again, the function must contain String inputs for the full path and names of the input and output files. Parameters could also be specified as input arguments of the function. For example: &lt;br /&gt;
&lt;br /&gt;
 foobar('%input','%output')&lt;br /&gt;
 foobar(.1,'%input','%output')&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
=== README File ===&lt;br /&gt;
&lt;br /&gt;
A README file accompanying each submission should contain explicit instructions on how to to run the program (as well as contact information, etc.). In particular, each command line to run should be specified, using %input for the input sound file and %output for the resulting text file.&lt;br /&gt;
&lt;br /&gt;
For instance, to test the program foobar with different values for parameters param1, the README file would look like:&lt;br /&gt;
&lt;br /&gt;
 foobar -param1 .1 -i %input -o %output&lt;br /&gt;
 foobar -param1 .15 -i %input -o %output&lt;br /&gt;
 foobar -param1 .2 -i %input -o %output&lt;br /&gt;
 foobar -param1 .25 -i %input -o %output&lt;br /&gt;
 foobar -param1 .3 -i %input -o %output&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
For a submission using MATLAB, the README file could look like:&lt;br /&gt;
&lt;br /&gt;
 matlab -r &amp;quot;foobar(.1,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.15,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.2,'%input','%output');quit;&amp;quot; &lt;br /&gt;
 matlab -r &amp;quot;foobar(.25,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 matlab -r &amp;quot;foobar(.3,'%input','%output');quit;&amp;quot;&lt;br /&gt;
 ...&lt;br /&gt;
&lt;br /&gt;
The different command lines to evaluate the performance of each parameter set over the whole database will be generated automatically from each line in the README file containing both '%input' and '%output' strings.&lt;br /&gt;
&lt;br /&gt;
== Evaluation Procedures ==&lt;br /&gt;
&lt;br /&gt;
The evaluation methods are taken from the beat evaluation toolbox and&lt;br /&gt;
are described in the following technical report: &lt;br /&gt;
&lt;br /&gt;
 M. E. P. Davies, N. Degara and M. D. Plumbley. &amp;quot;Evaluation methods for musical audio beat tracking algorithms&amp;quot;. [http://www.elec.qmul.ac.uk/people/markp/2009/DaviesDegaraPlumbley09-evaluation-tr.pdf ''Technical Report C4DM-TR-09-06'']. This link now works! :)&lt;br /&gt;
&lt;br /&gt;
For further details on the specifics of the methods please refer to the&lt;br /&gt;
paper. However, here is a brief summary with appropriate references:&lt;br /&gt;
&lt;br /&gt;
*'''F-measure''' - the standard calculation as used in onset evaluation but&lt;br /&gt;
with a 70ms window. &lt;br /&gt;
&lt;br /&gt;
 S. Dixon, &amp;quot;Onset detection revisited,&amp;quot; in ''Proceedings of 9th&lt;br /&gt;
 International Conference on Digital Audio Effects (DAFx)'', Montreal,&lt;br /&gt;
 Canada, pp. 133-137, 2006.&lt;br /&gt;
&lt;br /&gt;
 S. Dixon, &amp;quot;Evaluation of audio beat tracking system beatroot,&amp;quot; ''Journal&lt;br /&gt;
 of New Music Research'', vol. 36, no. 1, pp. 39-51, 2007.&lt;br /&gt;
&lt;br /&gt;
*'''Cemgil''' - beat accuracy is calculated using a Gaussian error function&lt;br /&gt;
with 40ms standard deviation.&lt;br /&gt;
&lt;br /&gt;
 A. T. Cemgil, B. Kappen, P. Desain, and H. Honing, &amp;quot;On tempo tracking:&lt;br /&gt;
 Tempogram representation and Kalman filtering,&amp;quot; ''Journal Of New Music&lt;br /&gt;
 Research'', vol. 28, no. 4, pp. 259-273, 2001&lt;br /&gt;
 &lt;br /&gt;
*'''Goto''' - binary decision of correct or incorrect tracking based on&lt;br /&gt;
statistical properties of a beat error sequence.&lt;br /&gt;
&lt;br /&gt;
 M. Goto and Y. Muraoka, &amp;quot;Issues in evaluating beat tracking systems,&amp;quot; in&lt;br /&gt;
 ''Working Notes of the IJCAI-97 Workshop on Issues in AI and Music -&lt;br /&gt;
 Evaluation and Assessment'', 1997, pp. 9-16.&lt;br /&gt;
&lt;br /&gt;
*'''PScore''' - McKinney's impulse train cross-correlation method as used in&lt;br /&gt;
2006.&lt;br /&gt;
&lt;br /&gt;
 M. F. McKinney, D. Moelants, M. E. P. Davies, and A. Klapuri,&lt;br /&gt;
 &amp;quot;Evaluation of audio beat tracking and music tempo extraction&lt;br /&gt;
 algorithms,&amp;quot; ''Journal of New Music Research'', vol. 36, no. 1, pp. 1-16,&lt;br /&gt;
 2007.&lt;br /&gt;
&lt;br /&gt;
*'''CMLc''', '''CMLt''', '''AMLc''', '''AMLt''' - continuity-based evaluation methods based on&lt;br /&gt;
the longest continuously correctly tracked section. &lt;br /&gt;
&lt;br /&gt;
 S. Hainsworth, &amp;quot;Techniques for the automated analysis of musical audio,&amp;quot;&lt;br /&gt;
 Ph.D. dissertation, Department of Engineering, Cambridge University,&lt;br /&gt;
 2004.&lt;br /&gt;
&lt;br /&gt;
 A. P. Klapuri, A. Eronen, and J. Astola, &amp;quot;Analysis of the meter of&lt;br /&gt;
 acoustic musical signals,&amp;quot; IEEE Transactions on Audio, Speech and&lt;br /&gt;
 Language Processing, vol. 14, no. 1, pp. 342-355, 2006.&lt;br /&gt;
&lt;br /&gt;
*'''D''', '''Dg''' - information based criteria based on analysis of a beat error&lt;br /&gt;
histogram (note the results are measured in 'bits' and not percentages),&lt;br /&gt;
see the technical report for a description.&lt;br /&gt;
&lt;br /&gt;
== Relevant Development Collections ==&lt;br /&gt;
You can find it here:&lt;br /&gt;
&lt;br /&gt;
https://www.music-ir.org/evaluation/MIREX/data/2006/beat/&lt;br /&gt;
&lt;br /&gt;
User: beattrack Password: b34trx&lt;br /&gt;
&lt;br /&gt;
https://www.music-ir.org/evaluation/MIREX/data/2006/tempo/&lt;br /&gt;
&lt;br /&gt;
User: tempo Password: t3mp0&lt;br /&gt;
&lt;br /&gt;
Data has been uploaded in both .tgz and .zip format.&lt;br /&gt;
&lt;br /&gt;
== Time and hardware limits ==&lt;br /&gt;
Due to the potentially high number of participants in this and other audio tasks, hard limits on the runtime of submissions will be imposed.&lt;br /&gt;
&lt;br /&gt;
A hard limit of 12 hours will be imposed on analysis times. Submissions exceeding this limit may not receive a result.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Submission opening date ==&lt;br /&gt;
&lt;br /&gt;
Friday August 5th 2012&lt;br /&gt;
&lt;br /&gt;
== Submission closing date ==&lt;br /&gt;
Friday September 2nd 2012&lt;br /&gt;
&lt;br /&gt;
== Potential Participants ==&lt;br /&gt;
name / email&lt;/div&gt;</summary>
		<author><name>Matthew Davies</name></author>
		
	</entry>
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