2007:Audio Artist Identification - Revision history

IMIRSELBot: Robot: Automated text replacement (-\[\[([A-Z][^:]+)\]\] +2007:\1)

2010-05-13T21:57:01Z

Robot: Automated text replacement (-\[\[([A-Z][^:]+)\]\] +2007:\1)

IMIRSELBot: Robot: Automated text replacement (-\[\[([A-Z][^:]+)\]\] +2007:\1)

2010-05-13T18:17:00Z

Robot: Automated text replacement (-\[\[([A-Z][^:]+)\]\] +2007:\1)

Will: /* Participants */

2007-09-26T03:16:35Z

‎Participants

Will: /* Participants */

2007-09-26T02:59:56Z

‎Participants

AndreasEhmann: /* Data */

2007-08-22T20:27:22Z

‎Data

AndreasEhmann: /* Status */

2007-08-22T20:25:57Z

‎Status

JDownie: /* Pre-trained submissions */

2007-08-13T22:02:29Z

‎Pre-trained submissions

JDownie: /* Classification output file */

2007-08-13T21:59:22Z

‎Classification output file

JDownie: /* Training list file */

2007-08-13T21:58:38Z

‎Training list file

JDownie: /* Implementation details */

2007-08-13T21:56:50Z

‎Implementation details

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 Related MIREX 2007 task proposals:
 * [[2007:Audio Music Mood Classification]]
-* [[Audio Music Similarity and Retrieval]]
+* [[2007:Audio Music Similarity and Retrieval]]
-* [[Audio Genre Classification]]
+* [[2007:Audio Genre Classification]]
 Please feel free to edit this page.

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 Related MIREX 2007 task proposals:
-* [[Audio Music Mood Classification]]
+* [[2007:Audio Music Mood Classification]]
 * [[Audio Music Similarity and Retrieval]]
 * [[Audio Genre Classification]]

@@ Line 160: / Line 160: @@
 * James Bergstra (bergstrj at iro umontreal ca)
 * Vitor Soares (''firstname.lastname''@clustermedialabs.com)
 * ...

@@ Line 160: / Line 160: @@
 * James Bergstra (bergstrj at iro umontreal ca)
 * Vitor Soares (''firstname.lastname''@clustermedialabs.com)
 * ...

@@ Line 12: / Line 12: @@
 == Data ==
-Collection statistics to be announced shortly. A new collection produced by IMIRSEL will be used and is expected to contain ~3000 examples organized into 100 or more artists. Each artist will have a minimum population of 3 albums and a minimum of 30 tracks (10 per album).
+Collection statistics: 3150 30-second 22.05kHz mono wav audio clips drawn from 105 artists (30 clips per artist drawn from 3 albums).
 == Evaluation ==

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 * Any required environments (and versions) such as Matlab, Java, Python, Bash, Ruby etc.
-=== Pre-trained submissions ===
 == Time and hardware limits ==

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 Participating algorithms should produce a simple ASCII list file identical in
 format to the Training list file. This file will contain one path per line,
-followed by a tab character and the genre label, again with no header line.
+followed by a tab character and the artist label, again with no header line.
 E.g. <example path and filename>\t<genre classification>

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 The list file passed for model training will be a simple ASCII list
 file. This file will contain one path per line, followed by a tab character and
-the genre label, again with no header line.
+the artist label, again with no header line.
-E.g. <example path and filename>\t<genre classification>
+E.g. <example path and filename>\t<artist classification>
 ==== Test (classification) list file ====

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 Scratch folders will be provided for all submissions for the storage of feature files and any model files to be produced. Executables will have to accept the path to their scratch folder as a command line parameter. Executables will also have to track which feature files correspond to which audio files internally. To facilitate this process, unique filenames will be assigned to each audio track.
-The audio files to be used in the task will be specified in a simple ASCII list file. For feature extraction and classification this file will contain one path per line with no header line. For model training this file will contain one path per line, followed by a tab character and the genre label, again with no header line. Executables will have to accept the path to these list files as a command line parameter. The formats for the list files are specified below.
+The audio files to be used in the task will be specified in a simple ASCII list file. For feature extraction and classification this file will contain one path per line with no header line. For model training this file will contain one path per line, followed by a tab character and the artist label, again with no header line. Executables will have to accept the path to these list files as a command line parameter. The formats for the list files are specified below.
 Algorithms should divide their feature extraction and training/classification into separate runs. This will facilitate a single feature extraction step for the task, while training and classification can be run for each cross-validation fold.
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 Multi-processor compute nodes (2, 4 or 8 cores) will be used to run this task. Hence, participants should attempt to use parrallelism where-ever possible. Ideally, the number of threads to use should be specified as a command line parameter. Alternatively, implementations may be provided in hard-coded 2, 4 or 8 thread configurations. Single threaded submissions will, of course, be accepted but may be disadvantaged by time constraints.
 === I/O formats ===