2026:Audio Downbeat Estimation

Description

Standardized Evaluation Suites: ismir-mirex/mirex-evaluation.

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.

Submitted algorithms will be evaluated in terms of their accuracy in finding downbeat locations (only) as annotated by musical experts across several diverse datasets.

Data

Test Data

Participants are not allowed to use any split of the following datasets for training, validation, model selection, parameter tuning, or any other development purpose:

GTZAN: The GTZAN dataset contains 1,000 30-second excerpts covering ten music genres. It provides a broad genre-balanced collection for evaluating beat and downbeat estimation systems beyond ballroom dance music.

Hidden test set: We may include an additional hidden test set, mainly consisting of pop-style music. Details about this hidden set will be announced later if it is included in the evaluation.

Other Collections

Other collections may be used, but participants must clearly state their dataset usage and, when applicable, the data collection process in the extended abstract.

Ballroom 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. We removed duplicates from the dataset as suggested by Bob Sturm, which finally yields 685 excerpts. We are using the audio files available here (see Gouyon et al. (2006)) and ground truth annotations available here (see Krebs et al. (2013)).

HarmonixSet 912 pop songs with beat, downbeat and structure annotations (Annotation Files, Dataset).

Turkish Data The Turkish corpus is an extended version of the annotated data used in Srinivasamurthy et al. (2014). It includes 82 excerpts of one minute length each, and each piece belongs to one of three rhythm classes that are referred to as usul in Turkish Art music. 32 pieces are in the 9/8-usul Aksak, 20 pieces in the 10/8-usul Curcuna, 30 samples in the 8/8-usul Düyek.

Cretan Data 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 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, 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 assumed to occur in all of the 42 songs in this corpus.

Carnatic Data The Carnatic music dataset is a subset of the CompMusic 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.

HJDB The HJDB dataset contains 235 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. For further information see Hockman et al (2012).

Audio Formats

The data are monophonic sound files

• CD-quality (PCM, 16-bit, 44100 Hz) for all except Ballroom (originally lower quality, but resampled to 44100 Hz)
• single channel (mono)

Submission Format

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.

Input Data

Participating algorithms will have to read audio in the following format:

• Sample rate: 44.1 KHz
• Sample size: 16 bit
• Number of channels: 1 (mono)
• Encoding: WAV

Output Data

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.

Output File Format (Audio Downbeat Estimation)

The downbeat output file format is an ASCII text format. Each downbeat time is specified, in seconds, on its own line. Specifically,

<downbeat time (in seconds)>\n

where \n denotes the end of line. The < and > characters are not included. An example output file would look something like:

0.243
1.486
2.729

Algorithm Calling Format

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:

foobar %input %output
foobar -i %input -o %output

Moreover, if your submission takes additional parameters, such as a detection threshold, foobar could be called like:

foobar .1 %input %output
foobar -param1 .1 -i %input -o %output  

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:

foobar('%input','%output')
foobar(.1,'%input','%output')

README File

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.

For instance, to test the program foobar with different values for parameters param1, the README file would look like:

foobar -param1 .1 -i %input -o %output
foobar -param1 .15 -i %input -o %output
foobar -param1 .2 -i %input -o %output
foobar -param1 .25 -i %input -o %output
foobar -param1 .3 -i %input -o %output
...

For a submission using MATLAB, the README file could look like:

matlab -r "foobar(.1,'%input','%output');quit;"
matlab -r "foobar(.15,'%input','%output');quit;"
matlab -r "foobar(.2,'%input','%output');quit;" 
matlab -r "foobar(.25,'%input','%output');quit;"
matlab -r "foobar(.3,'%input','%output');quit;"
...

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.

Evaluation Procedure

For the evaluation procedure we will use

• F-measure - the standard calculation as used in onset and beat tracking evaluation with a +/-70ms window, see Dixon (2007).

Given the high diversity of musical styles included in the task, results will be reported per each individual dataset.

Time and hardware limits

Due to the potentially high number of participants in this and other audio tasks, limits on the runtime of submissions may be imposed. Specific details will be announced with the evaluation instructions.

Bibliography

S. Dixon, F. Gouyon and G. Widmer, Towards Characterisation of Music via Rhythmic Patterns, In Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR 2004), pp 509-516.

S. Dixon, Evaluation of audio beat tracking system BeatRoot, Journal of New Music Research, vol. 36, no. 1, pp. 39-51, 2007.

J. A. Hockman, M. E. P. Davies, I. Fujinaga.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.

F. Krebs, S. Boeck, and G. Widmer, 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.

M. Mauch, C. Cannam, M. E. P. Davies, S. Dixon, C. Harte, S. Kolozali and D. Tidhar, OMRAS2 Metadata Project 2009, Late-breaking session at the 10th International Conference on Music Information Retrieval, 2009.

A. Srinivasamurthy, A. Holzapfel, and Xavier Serra, 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.

F. Gouyon, A. Klapuri, S. Dixon, M. Alonso, G. Tzanetakis, C. Uhle, and P. Cano. An experimental comparison of audio tempo induction algorithms. IEEE Transactions on Audio, Speech and Language Processing 14(5), pp.1832-1844, 2006.