View source for 2010:Audio Cover Song Identification

=2010 AUDIO COVER SONG IDENTIFICATION TASK OVERVIEW=

The text of this section is copied from the 2009 page. Please add your comments and discussions for 2010. 

The Audio Cover Song task was a new task for MIREX 2006 and was last run in 2008. It was closely related to the [[2010:Audio Music Similarity and Retrieval]] (AMS) task as the cover songs were embedded in the Audio Music Similarity and Retrieval test collection. 


==Task Description==
Within the 1000 pieces in the Audio Cover Song database, there are embedded 30 different "cover songs" each represented by 11 different "versions" for a total of 330 audio files (16bit, monophonic, 22.05khz, wav). The "cover songs" represent a variety of genres (e.g., classical, jazz, gospel, rock, folk-rock, etc.) and the variations span a variety of styles and orchestrations. 

Using each of these cover song files in turn as as the "seed/query" file, we will examine the returned lists of items for the presence of the other 10 versions of the "seed/query" file.


On top of the previous Audio Cover Song dataset, we are going to use the  [http://www.mazurka.org.uk/ Mazurka dataset]. We are going to randomly choose 11 versions from 49 mazurkas and run it as a separate subtask. The I/O format will be the same as previous years. Systems will return a distance matrix of 539x539.


=== Task specific mailing list ===
A specific mailing list is provided for the discussion of this task and related tasks ( [[2010:Audio Classification (Test/Train) tasks]], [[2010:Audio_Cover_Song_Identification]], [[2010:Audio_Tag_Classification]], [[2010:Audio_Music_Similarity_and_Retrieval]]) at: [https://mail.lis.uiuc.edu/mailman/listinfo/mrx-com00 https://mail.lis.uiuc.edu/mailman/listinfo/mrx-com00]. If you wish to participate in any of these tasks please sign up to this mailing listas discussion of the task format and evaluation should be conducted there.


=== Command Line Calling Format ===

 $ /path/to/submission <collection_list_file> <query_list_file> <working_directory> <output_file>
     '''<collection_list_file>''': Text file containing 1000 full path file names for the
                             1000 audio files in the collection (including the 330 
                             query documents).
                             '''Example: /path/to/coversong/collection.txt'''
     '''<query_list_file>'''     : Text file containing the 330 full path file names for the 
                             330 query documents.
                             '''Example: /path/to/coversong/queries.txt'''
     '''<working_directory>'''   : Full path to a temporary directory where submission will 
                             have write access for caching features or calculations.
                             '''Example: /tmp/submission_id/'''
     '''<output_file>'''         : Full path to file where submission should output the similarity 
                             matrix (1000 header rows + 330 x 1000 data matrix).
                             '''Example: /path/to/coversong/results/submission_id.txt'''

=== Input Files ===

The collection lists file format will be of the form: 

 /path/to/audio/file/000.wav\n
 /path/to/audio/file/001.wav\n
 /path/to/audio/file/002.wav\n
 ... * 996 rows omitted * ...
 /path/to/audio/file/999.wav\n

The query lists file format will be of the form: 

 /path/to/audio/file/182.wav\n
 /path/to/audio/file/245.wav\n
 /path/to/audio/file/432.wav\n
 ... * 326 rows omitted * ...
 /path/to/audio/file/973.wav\n

For a total of 330 rows -- query ids are randomly assigned from the pool of 1000 collection ids.

Lines will be terminated by a '\n' character.

=== Output File ===
The only output will be a '''distance''' matrix file that is 330 rows by 1000 columns in the following format: 



<pre>
Distance matrix header text with system name
1\t</path/to/audio/file/track1.wav>
2\t</path/to/audio/file/track2.wav>
3\t</path/to/audio/file/track3.wav>
4\t</path/to/audio/file/track4.wav>
...
N\t</path/to/audio/file/trackN.wav>
Q/R\t1\t2\t3\t4\t...\tN
1\t<dist 1 to 1>\t<dist 1 to 2>\t<dist 1 to 3>\t<dist 1 to 4>\t...\t<dist 1 to N>
3\t<dist 3 to 2>\t<dist 3 to 2>\t<dist 3 to 3>\t<dist 3 to 4>\t...\t<dist 3 to N>
</pre>

where N is 1000 (number of candidate tracks in dataset) and the queries are drawn from this set (and bear the same track indexes if possible).

which might look like:

<pre>
Example distance matrix 0.1
1    /path/to/audio/file/track1.wav
2    /path/to/audio/file/track2.wav
3    /path/to/audio/file/track3.wav
4    /path/to/audio/file/track4.wav
5    /path/to/audio/file/track5.wav
Q/R   1        2        3        4        5
1     0.00000  1.24100  0.2e-4   0.42559  0.21313
3     50.2e-4  0.62640  0.00000  0.38000  0.15152
</pre>

Note that indexes of the queries refer back to the track list at the top of the distance matrix file to identify the query track. However, as long as you ensure that the query songs are listed in exactly the same order as they appear in the query list file you are passed we will be able to interpret the data.

All distances should be zero or positive (0.0+) and should not be infinite or NaN. Values should be separated by a TAB.

As N (collection searched for covers) is 1000 and there are 330 original tracks, the distance matrix should be preceded by a system name, 1000 rows of file paths and should be composed of 1000 columns of distance (separated by tab characters) and 330 rows (one for each original track query). Each row corresponds to a particular query song (the track to find covers of).

==Evaluation==

We will employ the same measures used in [[2007:Audio Cover Song]].