https://www.music-ir.org/mirex/w/api.php?action=feedcontributions&user=Asriver&feedformat=atomMIREX Wiki - User contributions [en]2024-03-28T16:47:41ZUser contributionsMediaWiki 1.31.1https://www.music-ir.org/mirex/w/index.php?title=2021:Audio_Beat_Tracking&diff=133892021:Audio Beat Tracking2021-10-26T19:11:54Z<p>Asriver: /* Potential Participants */</p>
<hr />
<div>== Description ==<br />
The text of this section was copied from the 2012 Wiki. Please add your comments and discussion at the bottom of this page.<br />
<br />
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. <br />
<br />
== Data ==<br />
=== Collections ===<br />
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.<br />
<br />
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.<br />
<br />
The third collection was assembled and donated in 2012. This dataset contains 217 excerpts around 40s each, of which 19 are "easy" and the remaining 198 are "hard". The harder excerpts were drawn from the following musical styles: Romantic music, film soundtracks, blues, chanson and solo guitar. <br />
<br />
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!<br />
But don't be deterred, this is for the good of beat tracking. <br />
<br />
You can read in detail about how the dataset was made here:<br />
[http://dx.doi.org/10.1109/TASL.2012.2205244 ''Selective Sampling for Beat Tracking Evaluation'']<br />
<br />
=== Audio Formats ===<br />
<br />
The data are monophonic sound files, with the associated onset times and data about the annotation robustness.<br />
<br />
* CD-quality (PCM, 16-bit, 44100 Hz)<br />
* single channel (mono)<br />
* file length between 2 and 36 seconds (total time: 14 minutes) <br />
<br />
<br />
== Submission Format ==<br />
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.<br />
<br />
=== Input Data ===<br />
Participating algorithms will have to read audio in the following format:<br />
<br />
* Sample rate: 44.1 KHz<br />
* Sample size: 16 bit<br />
* Number of channels: 1 (mono)<br />
* Encoding: WAV <br />
<br />
=== Output Data ===<br />
<br />
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.<br />
<br />
=== Output File Format (Audio Beat tracking) ===<br />
<br />
The Beat Tracking output file format is an ASCII text format. Each beat time is specified, in seconds, on its own line. Specifically, <br />
<br />
<beat time(in seconds)>\n<br />
<br />
where \n denotes the end of line. The < and > characters are not included. An example output file would look something like:<br />
<br />
0.243<br />
0.486<br />
0.729<br />
<br />
=== Algorithm Calling Format ===<br />
<br />
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:<br />
<br />
foobar %input %output<br />
foobar -i %input -o %output<br />
<br />
Moreover, if your submission takes additional parameters, such as a detection threshold, foobar could be called like:<br />
<br />
foobar .1 %input %output<br />
foobar -param1 .1 -i %input -o %output <br />
<br />
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: <br />
<br />
foobar('%input','%output')<br />
foobar(.1,'%input','%output')<br />
<br />
<br />
=== README File ===<br />
<br />
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.<br />
<br />
For instance, to test the program foobar with different values for parameters param1, the README file would look like:<br />
<br />
foobar -param1 .1 -i %input -o %output<br />
foobar -param1 .15 -i %input -o %output<br />
foobar -param1 .2 -i %input -o %output<br />
foobar -param1 .25 -i %input -o %output<br />
foobar -param1 .3 -i %input -o %output<br />
...<br />
<br />
For a submission using MATLAB, the README file could look like:<br />
<br />
matlab -r "foobar(.1,'%input','%output');quit;"<br />
matlab -r "foobar(.15,'%input','%output');quit;"<br />
matlab -r "foobar(.2,'%input','%output');quit;" <br />
matlab -r "foobar(.25,'%input','%output');quit;"<br />
matlab -r "foobar(.3,'%input','%output');quit;"<br />
...<br />
<br />
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.<br />
<br />
== Evaluation Procedures ==<br />
<br />
The evaluation methods are taken from the beat evaluation toolbox and<br />
are described in the following technical report: <br />
<br />
M. E. P. Davies, N. Degara and M. D. Plumbley. "Evaluation methods for musical audio beat tracking algorithms". [http://www.elec.qmul.ac.uk/people/markp/2009/DaviesDegaraPlumbley09-evaluation-tr.pdf ''Technical Report C4DM-TR-09-06'']. This link now works! :)<br />
<br />
For further details on the specifics of the methods please refer to the<br />
paper. However, here is a brief summary with appropriate references:<br />
<br />
*'''F-measure''' - the standard calculation as used in onset evaluation but<br />
with a 70ms window. <br />
<br />
S. Dixon, "Onset detection revisited," in ''Proceedings of 9th<br />
International Conference on Digital Audio Effects (DAFx)'', Montreal,<br />
Canada, pp. 133-137, 2006.<br />
<br />
S. Dixon, "Evaluation of audio beat tracking system beatroot," ''Journal<br />
of New Music Research'', vol. 36, no. 1, pp. 39-51, 2007.<br />
<br />
*'''Cemgil''' - beat accuracy is calculated using a Gaussian error function<br />
with 40ms standard deviation.<br />
<br />
A. T. Cemgil, B. Kappen, P. Desain, and H. Honing, "On tempo tracking:<br />
Tempogram representation and Kalman filtering," ''Journal Of New Music<br />
Research'', vol. 28, no. 4, pp. 259-273, 2001<br />
<br />
*'''Goto''' - binary decision of correct or incorrect tracking based on<br />
statistical properties of a beat error sequence.<br />
<br />
M. Goto and Y. Muraoka, "Issues in evaluating beat tracking systems," in<br />
''Working Notes of the IJCAI-97 Workshop on Issues in AI and Music -<br />
Evaluation and Assessment'', 1997, pp. 9-16.<br />
<br />
*'''PScore''' - McKinney's impulse train cross-correlation method as used in<br />
2006.<br />
<br />
M. F. McKinney, D. Moelants, M. E. P. Davies, and A. Klapuri,<br />
"Evaluation of audio beat tracking and music tempo extraction<br />
algorithms," ''Journal of New Music Research'', vol. 36, no. 1, pp. 1-16,<br />
2007.<br />
<br />
*'''CMLc''', '''CMLt''', '''AMLc''', '''AMLt''' - continuity-based evaluation methods based on<br />
the longest continuously correctly tracked section. <br />
<br />
S. Hainsworth, "Techniques for the automated analysis of musical audio,"<br />
Ph.D. dissertation, Department of Engineering, Cambridge University,<br />
2004.<br />
<br />
A. P. Klapuri, A. Eronen, and J. Astola, "Analysis of the meter of<br />
acoustic musical signals," IEEE Transactions on Audio, Speech and<br />
Language Processing, vol. 14, no. 1, pp. 342-355, 2006.<br />
<br />
*'''D''', '''Dg''' - information based criteria based on analysis of a beat error<br />
histogram (note the results are measured in 'bits' and not percentages),<br />
see the technical report for a description.<br />
<br />
== Relevant Development Collections ==<br />
You can find it here:<br />
<br />
(data has been uploaded in both .tgz and .zip format)<br />
<br />
''User: beattrack Password: b34trx''<br />
<br />
https://www.music-ir.org/evaluation/MIREX/data/2006/beat/beattrack_train_2006.tgz OR<br />
<br />
https://www.music-ir.org/evaluation/MIREX/data/2006/beat/beattrack_train_2006.zip<br />
<br />
''User: tempo Password: t3mp0''<br />
<br />
https://www.music-ir.org/evaluation/MIREX/data/2006/tempo/tempo_train_2006.tgz OR<br />
<br />
https://www.music-ir.org/evaluation/MIREX/data/2006/tempo/tempo_train_2006.zip<br />
<br />
== Time and hardware limits ==<br />
Due to the potentially high number of participants in this and other audio tasks, hard limits on the runtime of submissions will be imposed.<br />
<br />
A hard limit of 12 hours will be imposed on analysis times. Submissions exceeding this limit may not receive a result.<br />
<br />
<br />
== Potential Participants ==<br />
name / email<br />
<br />
Ju-Chiang Wang / ju-chiang.wang@bytedance.com<br />
<br />
== Discussion ==<br />
name / email</div>Asriverhttps://www.music-ir.org/mirex/w/index.php?title=2021:Audio_Downbeat_Estimation&diff=133872021:Audio Downbeat Estimation2021-10-26T19:11:23Z<p>Asriver: /* Potential Participants */</p>
<hr />
<div>== Description ==<br />
<br />
This text has been adapted from the Audio Beat Tracking Wiki page. Please add your comments and discussion at the bottom of this page.<br />
<br />
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.<br />
<br />
Submitted algorithms will be evaluated in terms of their accuracy in finding downbeat locations (only) as annotated by musical experts across several diverse datasets.<br />
<br />
'''Update 22/07/14''' A small set of training data is now available. Please see [[#Example_Data]]<br />
<br />
== Data ==<br />
<br />
=== Collections ===<br />
'''Ballroom'''<br />
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 [http://media.aau.dk/null_space_pursuits/2014/01/ballroom-dataset.html Bob Sturm], which finally yields '''685''' excerpts. <br />
We are using the audio files available [http://mtg.upf.edu/ismir2004/contest/tempoContest/node5.html here] (see Gouyon et al (2006)) and ground truth annotations available [https://github.com/CPJKU/BallroomAnnotations here] (see Krebs et al (2013)).<br />
<br />
'''Isophonics (Beatles only)'''<br />
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. <br />
This dataset contains '''179''' complete songs (all except Revolution 9), the majority of which are in 4/4.<br />
For further information see Mauch et al (2009).<br />
<br />
'''Turkish Data'''<br />
The Turkish corpus is an extended version of the annotated data used in Srinivasamurthy et al. (2014). It includes '''82''' excerpts of one<br />
minute length each, and each piece belongs to one of three<br />
rhythm classes that are referred to as usul in Turkish Art<br />
music. 32 pieces are in the 9/8-usul Aksak, 20 pieces<br />
in the 10/8-usul Curcuna, 30 samples in the 8/8-usul<br />
Düyek.<br />
<br />
'''Cretan Data'''<br />
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<br />
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,<br />
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<br />
assumed to occur in all of the 42 songs in this corpus.<br />
<br />
'''Carnatic Data'''<br />
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. <br />
<br />
'''HJDB'''<br />
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. <br />
For further information see Hockman et al (2012).<br />
<br />
In total this makes '''1341''' excerpts (of which 221 are full length songs).<br />
<br />
=== Audio Formats ===<br />
<br />
The data are monophonic sound files<br />
<br />
* CD-quality (PCM, 16-bit, 44100 Hz) for all except Ballroom (originally lower quality, but resampled to 44100 Hz)<br />
* single channel (mono)<br />
<br />
== Submission Format ==<br />
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.<br />
<br />
=== Input Data ===<br />
Participating algorithms will have to read audio in the following format:<br />
<br />
* Sample rate: 44.1 KHz<br />
* Sample size: 16 bit<br />
* Number of channels: 1 (mono)<br />
* Encoding: WAV <br />
<br />
=== Output Data ===<br />
<br />
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.<br />
<br />
=== Output File Format (Audio Downbeat Estimation) ===<br />
<br />
The downbeat output file format is an ASCII text format. Each downbeat time is specified, in seconds, on its own line. Specifically, <br />
<br />
<downbeat time (in seconds)>\n<br />
<br />
where \n denotes the end of line. The < and > characters are not included. An example output file would look something like:<br />
<br />
0.243<br />
1.486<br />
2.729<br />
<br />
=== Algorithm Calling Format ===<br />
<br />
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:<br />
<br />
foobar %input %output<br />
foobar -i %input -o %output<br />
<br />
Moreover, if your submission takes additional parameters, such as a detection threshold, foobar could be called like:<br />
<br />
foobar .1 %input %output<br />
foobar -param1 .1 -i %input -o %output <br />
<br />
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: <br />
<br />
foobar('%input','%output')<br />
foobar(.1,'%input','%output')<br />
<br />
=== README File ===<br />
<br />
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.<br />
<br />
For instance, to test the program foobar with different values for parameters param1, the README file would look like:<br />
<br />
foobar -param1 .1 -i %input -o %output<br />
foobar -param1 .15 -i %input -o %output<br />
foobar -param1 .2 -i %input -o %output<br />
foobar -param1 .25 -i %input -o %output<br />
foobar -param1 .3 -i %input -o %output<br />
...<br />
<br />
For a submission using MATLAB, the README file could look like:<br />
<br />
matlab -r "foobar(.1,'%input','%output');quit;"<br />
matlab -r "foobar(.15,'%input','%output');quit;"<br />
matlab -r "foobar(.2,'%input','%output');quit;" <br />
matlab -r "foobar(.25,'%input','%output');quit;"<br />
matlab -r "foobar(.3,'%input','%output');quit;"<br />
...<br />
<br />
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.<br />
<br />
== Evaluation Procedure ==<br />
<br />
For the evaluation procedure we will use<br />
*'''F-measure''' - the standard calculation as used in onset and beat tracking evaluation with a +/-70ms window, see Dixon (2007).<br />
<br />
Given the high diversity of musical styles included in the task, results will be reported per each individual dataset. <br />
<br />
== Example Data ==<br />
<br />
A total of 20 beat and downbeat annotated 30s excerpts are available for participants. They serve as example data and are part of the test data, so please do not add them to your training data.<br />
The data (47MB) is available to download here:<br />
<br />
https://www.music-ir.org/evaluation/MIREX/data/2014/downbeat/downbeat_examples.zip<br />
<br />
''User:'' downbeat <br />
<br />
''Password:'' d0wn63at<br />
<br />
Due to the availability of the Ballroom and Beatles datasets, we only include examples of the remaining styles as follows:<br />
<br />
'''HJDB''': 5 excerpts<br />
<br />
'''Cretan''': 3 excerpts<br />
<br />
'''Carnatic''': 4 excerpts<br />
<br />
'''Turkish''': 8 excerpts (2x Aksak, 2x Curcuna, 2x Düyek, and 2x Sofyan).<br />
<br />
Please note the audio files are in ''.flac'' format.<br />
<br />
For each audio file, e.g. ''hjdb1.fla''c there is a corresponding annotation file ''hjdb1.txt'' <br />
<br />
Each ''.txt'' file contains timestamps corresponding to beat annotations and a label to denote the position in the bar. <br />
<br />
All beat times labelled '1' correspond to downbeats.<br />
<br />
== Time and hardware limits ==<br />
Due to the potentially high number of participants in this and other audio tasks, hard limits on the runtime of submissions will be imposed.<br />
<br />
A hard limit of 24 hours will be imposed on analysis times. Submissions exceeding this limit may not receive a result.<br />
<br />
== Potential Participants ==<br />
name / email<br />
<br />
Ju-Chiang Wang / ju-chiang.wang@bytedance.com<br />
<br />
== Discussion ==<br />
name / email<br />
<br />
= Bibliography =<br />
<br />
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.<br />
<br />
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.<br />
<br />
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.<br />
<br />
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.<br />
<br />
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.<br />
<br />
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.<br />
<br />
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.</div>Asriverhttps://www.music-ir.org/mirex/w/index.php?title=2021:Structural_Segmentation&diff=133862021:Structural Segmentation2021-10-26T19:10:47Z<p>Asriver: /* Potential Participants */</p>
<hr />
<div>== Description ==<br />
<br />
The aim of the MIREX structural segmentation evaluation is to identify the key structural sections in musical audio. The segment structure (or form) is one of the most important musical parameters. It is furthermore special because musical structure -- especially in popular music genres (e.g. verse, chorus, etc.) -- is accessible to everybody: it needs no particular musical knowledge. This task was first run in 2009.<br />
<br />
== Data == <br />
<br />
=== Collections ===<br />
* The MIREX 2009 Collection: 297 pieces, most of it derived from the work of the Beatles.<br />
<br />
* MIREX 2010 RWC collection. 100 pieces of popular music. There are two ground truths. The first is the one originally included with the RWC dataset. The explanation of the second set of annotations can be found at http://hal.inria.fr/docs/00/47/34/79/PDF/PI-1948.pdf. The second set of annotations contains no labels for segments, but rather provides an annotation of segment boundaries.<br />
<br />
* MIREX 2012 dataset. The new data set contains over 1,000 annotated pieces covering a range of musical styles. The majority of the pieces have been annotated by two independent annotators. <br />
<br />
=== Audio Formats ===<br />
<br />
* CD-quality (PCM, 16-bit, 44100 Hz)<br />
* single channel (mono)<br />
<br />
== Submission Format ==<br />
<br />
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.<br />
<br />
=== Input Data ===<br />
Participating algorithms will have to read audio in the following format:<br />
<br />
* Sample rate: 44.1 KHz<br />
* Sample size: 16 bit<br />
* Number of channels: 1 (mono)<br />
* Encoding: WAV <br />
<br />
=== Output Data ===<br />
<br />
The structural segmentation algorithms will return the segmentation in an ASCII text file for each input .wav audio file. The specification of this output file is immediately below.<br />
<br />
=== Output File Format (Structural Segmentation) ===<br />
<br />
The Structural Segmentation output file format is a tab-delimited ASCII text format. This is the same as Chris Harte's chord labelling files (.lab), and so is the same format as the ground truth as well. Onset and offset times are given in seconds, and the labels are simply letters: 'A', 'B', ... with segments referring to the same structural element having the same label.<br />
<br />
Three column text file of the format<br />
<br />
<onset_time(sec)>\t<offset_time(sec)>\t<label>\n<br />
<onset_time(sec)>\t<offset_time(sec)>\t<label>\n<br />
...<br />
<br />
where \t denotes a tab, \n denotes the end of line. The < and > characters are not included. An example output file would look something like:<br />
<br />
0.000 5.223 A<br />
5.223 15.101 B<br />
15.101 20.334 A<br />
<br />
=== Algorithm Calling Format ===<br />
<br />
The submitted algorithm must take as arguments a SINGLE .wav file to perform the structural segmentation 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:<br />
<br />
foobar %input %output<br />
foobar -i %input -o %output<br />
<br />
Moreover, if your submission takes additional parameters, foobar could be called like:<br />
<br />
foobar .1 %input %output<br />
foobar -param1 .1 -i %input -o %output <br />
<br />
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: <br />
<br />
foobar('%input','%output')<br />
foobar(.1,'%input','%output')<br />
<br />
=== README File ===<br />
<br />
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.<br />
<br />
For instance, to test the program foobar with a specific value for parameter param1, the README file would look like:<br />
<br />
foobar -param1 .1 -i %input -o %output<br />
<br />
For a submission using MATLAB, the README file could look like:<br />
<br />
matlab -r "foobar(.1,'%input','%output');quit;"<br />
<br />
== Evaluation Procedures ==<br />
At the last ISMIR conference [http://ismir2008.ismir.net/papers/ISMIR2008_219.pdf Lukashevich] proposed a measure for segmentation evaluation. Because of the complexity of the structural segmentation task definition, several different evaluation measures will be employed to address different aspects. It should be noted that none of the evaluation measures cares about the true labels of the sections: they only denote the clustering. This means that it does not matter if the systems produce true labels such as "chorus" and "verse", or arbitrary labels such as "A" and "B".<br />
<br />
=== Boundary retrieval ===<br />
'''Hit rate''' Found segment boundaries are accepted to be correct if they are within 0.5s ([http://ismir2007.ismir.net/proceedings/ISMIR2007_p051_turnbull.pdf Turnbull et al. ISMIR2007]) or 3s ([http://dx.doi.org/10.1109/TASL.2007.910781 Levy & Sandler TASLP2008]) from a border in the ground truth. Based on the matched hits, ''boundary retrieval recall rate'', ''boundary retrieval precision rate'', and ''boundary retrieval F-measure'' are be calculated.<br />
<br />
'''Median deviation''' Two median deviation measure between boundaries in the result and ground truth are calculated: ''median true-to-guess'' is the median time from boundaries in ground truth to the closest boundaries in the result, and ''median guess-to-true'' is similarly the median time from boundaries in the result to boundaries in ground truth. ([http://ismir2007.ismir.net/proceedings/ISMIR2007_p051_turnbull.pdf Turnbull et al. ISMIR2007])<br />
<br />
=== Frame clustering ===<br />
Both the result and the ground truth are handled in short frames (e.g., beat or fixed 100ms). All frame pairs in a structure description are handled. The pairs in which both frames are assigned to the same cluster (i.e., have the same label) form the sets <math>P_E</math> (for the system result) and <math>P_A</math> (for the ground truth). The ''pairwise precision rate'' can be calculated by <math>P = \frac{|P_E \cap P_A|}{|P_E|}</math>, ''pairwise recall rate'' by <math>R = \frac{|P_E \cap P_A|}{|P_A|}</math>, and ''pairwise F-measure'' by <math>F=\frac{2 P R}{P + R}</math>. ([http://dx.doi.org/10.1109/TASL.2007.910781 Levy & Sandler TASLP2008])<br />
<br />
=== Normalised conditional entropies ===<br />
Over- and under segmentation based evaluation measures proposed in [http://ismir2008.ismir.net/papers/ISMIR2008_219.pdf Lukashevich ISMIR2008].<br />
Structure descriptions are represented as frame sequences with the associated cluster information (similar to the Frame clustering measure). Confusion matrix between the labels in ground truth and the result is calculated. The matrix C is of size |L_A| * |L_E|, i.e., number of unique labels in the ground truth times number of unique labels in the result. From the confusion matrix, the joint distribution is calculated by normalising the values with the total number of frames F:<br />
<br />
<math>p_{i,j} = C_{i,j} / F</math><br />
<br />
Similarly, the two marginals are calculated:<br />
<br />
<math>p_i^a = \sum_{j=1}^{|L_E|} C{i,j}/F</math>, and<br />
<br />
<math>p_j^e = \sum_{i=1}^{|L_A|} C{i,j}/F</math><br />
<br />
Conditional distributions:<br />
<br />
<math>p_{i,j}^{a|e} = C_{i,j} / \sum_{i=1}^{|L_A|} C{i,j}</math>, and<br />
<br />
<math>p_{i,j}^{e|a} = C_{i,j} / \sum_{j=1}^{|L_E|} C{i,j}</math><br />
<br />
The conditional entropies will then be<br />
<br />
<math>H(E|A) = - \sum_{i=1}^{|L_A|} p_i^a \sum_{j=1}^{|L_E|} p_{i,j}^{e|a} \log_2(p_{i,j}^{e|a})</math>, and<br />
<br />
<math>H(A|E) = - \sum_{j=1}^{|L_E|} p_j^e \sum_{i=1}^{|L_A|} p_{i,j}^{a|e} \log_2(p_{i,j}^{a|e})</math><br />
<br />
The final evaluation measures will then be the oversegmentation score<br />
<br />
<math>S_O = 1 - \frac{H(E|A)}{\log_2(|L_E|)}</math> , and the undersegmentation score<br />
<br />
<math>S_U = 1 - \frac{H(A|E)}{\log_2(|L_A|)}</math><br />
<br />
== Relevant Development Collections == <br />
*Jouni Paulus's [http://www.cs.tut.fi/sgn/arg/paulus/structure.html structure analysis page] links to a corpus of 177 Beatles songs ([http://www.cs.tut.fi/sgn/arg/paulus/beatles_sections_TUT.zip zip file]). The Beatles annotations are not a part of the TUTstructure07 dataset. That dataset contains 557 songs, a list of which is available [http://www.cs.tut.fi/sgn/arg/paulus/TUTstructure07_files.html here].<br />
<br />
*Ewald Peiszer's [http://www.ifs.tuwien.ac.at/mir/audiosegmentation.html thesis page] links to a portion of the corpus he used: 43 non-Beatles pop songs (including 10 J-pop songs) ([http://www.ifs.tuwien.ac.at/mir/audiosegmentation/dl/ep_groundtruth_excl_Paulus.zip zip file]).<br />
<br />
These public corpora give a combined 220 songs.<br />
<br />
== Time and hardware limits ==<br />
Due to the potentially high number of participants in this and other audio tasks, hard limits on the runtime of submissions will be imposed.<br />
<br />
A hard limit of 24 hours will be imposed on analysis times. Submissions exceeding this limit may not receive a result.<br />
<br />
<br />
== Potential Participants ==<br />
<br />
name / email<br />
<br />
Ju-Chiang Wang / ju-chiang.wang@bytedance.com<br />
<br />
== Discussion ==</div>Asriverhttps://www.music-ir.org/mirex/w/index.php?title=2021:Structural_Segmentation&diff=133852021:Structural Segmentation2021-10-26T19:10:34Z<p>Asriver: /* Potential Participants */</p>
<hr />
<div>== Description ==<br />
<br />
The aim of the MIREX structural segmentation evaluation is to identify the key structural sections in musical audio. The segment structure (or form) is one of the most important musical parameters. It is furthermore special because musical structure -- especially in popular music genres (e.g. verse, chorus, etc.) -- is accessible to everybody: it needs no particular musical knowledge. This task was first run in 2009.<br />
<br />
== Data == <br />
<br />
=== Collections ===<br />
* The MIREX 2009 Collection: 297 pieces, most of it derived from the work of the Beatles.<br />
<br />
* MIREX 2010 RWC collection. 100 pieces of popular music. There are two ground truths. The first is the one originally included with the RWC dataset. The explanation of the second set of annotations can be found at http://hal.inria.fr/docs/00/47/34/79/PDF/PI-1948.pdf. The second set of annotations contains no labels for segments, but rather provides an annotation of segment boundaries.<br />
<br />
* MIREX 2012 dataset. The new data set contains over 1,000 annotated pieces covering a range of musical styles. The majority of the pieces have been annotated by two independent annotators. <br />
<br />
=== Audio Formats ===<br />
<br />
* CD-quality (PCM, 16-bit, 44100 Hz)<br />
* single channel (mono)<br />
<br />
== Submission Format ==<br />
<br />
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.<br />
<br />
=== Input Data ===<br />
Participating algorithms will have to read audio in the following format:<br />
<br />
* Sample rate: 44.1 KHz<br />
* Sample size: 16 bit<br />
* Number of channels: 1 (mono)<br />
* Encoding: WAV <br />
<br />
=== Output Data ===<br />
<br />
The structural segmentation algorithms will return the segmentation in an ASCII text file for each input .wav audio file. The specification of this output file is immediately below.<br />
<br />
=== Output File Format (Structural Segmentation) ===<br />
<br />
The Structural Segmentation output file format is a tab-delimited ASCII text format. This is the same as Chris Harte's chord labelling files (.lab), and so is the same format as the ground truth as well. Onset and offset times are given in seconds, and the labels are simply letters: 'A', 'B', ... with segments referring to the same structural element having the same label.<br />
<br />
Three column text file of the format<br />
<br />
<onset_time(sec)>\t<offset_time(sec)>\t<label>\n<br />
<onset_time(sec)>\t<offset_time(sec)>\t<label>\n<br />
...<br />
<br />
where \t denotes a tab, \n denotes the end of line. The < and > characters are not included. An example output file would look something like:<br />
<br />
0.000 5.223 A<br />
5.223 15.101 B<br />
15.101 20.334 A<br />
<br />
=== Algorithm Calling Format ===<br />
<br />
The submitted algorithm must take as arguments a SINGLE .wav file to perform the structural segmentation 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:<br />
<br />
foobar %input %output<br />
foobar -i %input -o %output<br />
<br />
Moreover, if your submission takes additional parameters, foobar could be called like:<br />
<br />
foobar .1 %input %output<br />
foobar -param1 .1 -i %input -o %output <br />
<br />
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: <br />
<br />
foobar('%input','%output')<br />
foobar(.1,'%input','%output')<br />
<br />
=== README File ===<br />
<br />
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.<br />
<br />
For instance, to test the program foobar with a specific value for parameter param1, the README file would look like:<br />
<br />
foobar -param1 .1 -i %input -o %output<br />
<br />
For a submission using MATLAB, the README file could look like:<br />
<br />
matlab -r "foobar(.1,'%input','%output');quit;"<br />
<br />
== Evaluation Procedures ==<br />
At the last ISMIR conference [http://ismir2008.ismir.net/papers/ISMIR2008_219.pdf Lukashevich] proposed a measure for segmentation evaluation. Because of the complexity of the structural segmentation task definition, several different evaluation measures will be employed to address different aspects. It should be noted that none of the evaluation measures cares about the true labels of the sections: they only denote the clustering. This means that it does not matter if the systems produce true labels such as "chorus" and "verse", or arbitrary labels such as "A" and "B".<br />
<br />
=== Boundary retrieval ===<br />
'''Hit rate''' Found segment boundaries are accepted to be correct if they are within 0.5s ([http://ismir2007.ismir.net/proceedings/ISMIR2007_p051_turnbull.pdf Turnbull et al. ISMIR2007]) or 3s ([http://dx.doi.org/10.1109/TASL.2007.910781 Levy & Sandler TASLP2008]) from a border in the ground truth. Based on the matched hits, ''boundary retrieval recall rate'', ''boundary retrieval precision rate'', and ''boundary retrieval F-measure'' are be calculated.<br />
<br />
'''Median deviation''' Two median deviation measure between boundaries in the result and ground truth are calculated: ''median true-to-guess'' is the median time from boundaries in ground truth to the closest boundaries in the result, and ''median guess-to-true'' is similarly the median time from boundaries in the result to boundaries in ground truth. ([http://ismir2007.ismir.net/proceedings/ISMIR2007_p051_turnbull.pdf Turnbull et al. ISMIR2007])<br />
<br />
=== Frame clustering ===<br />
Both the result and the ground truth are handled in short frames (e.g., beat or fixed 100ms). All frame pairs in a structure description are handled. The pairs in which both frames are assigned to the same cluster (i.e., have the same label) form the sets <math>P_E</math> (for the system result) and <math>P_A</math> (for the ground truth). The ''pairwise precision rate'' can be calculated by <math>P = \frac{|P_E \cap P_A|}{|P_E|}</math>, ''pairwise recall rate'' by <math>R = \frac{|P_E \cap P_A|}{|P_A|}</math>, and ''pairwise F-measure'' by <math>F=\frac{2 P R}{P + R}</math>. ([http://dx.doi.org/10.1109/TASL.2007.910781 Levy & Sandler TASLP2008])<br />
<br />
=== Normalised conditional entropies ===<br />
Over- and under segmentation based evaluation measures proposed in [http://ismir2008.ismir.net/papers/ISMIR2008_219.pdf Lukashevich ISMIR2008].<br />
Structure descriptions are represented as frame sequences with the associated cluster information (similar to the Frame clustering measure). Confusion matrix between the labels in ground truth and the result is calculated. The matrix C is of size |L_A| * |L_E|, i.e., number of unique labels in the ground truth times number of unique labels in the result. From the confusion matrix, the joint distribution is calculated by normalising the values with the total number of frames F:<br />
<br />
<math>p_{i,j} = C_{i,j} / F</math><br />
<br />
Similarly, the two marginals are calculated:<br />
<br />
<math>p_i^a = \sum_{j=1}^{|L_E|} C{i,j}/F</math>, and<br />
<br />
<math>p_j^e = \sum_{i=1}^{|L_A|} C{i,j}/F</math><br />
<br />
Conditional distributions:<br />
<br />
<math>p_{i,j}^{a|e} = C_{i,j} / \sum_{i=1}^{|L_A|} C{i,j}</math>, and<br />
<br />
<math>p_{i,j}^{e|a} = C_{i,j} / \sum_{j=1}^{|L_E|} C{i,j}</math><br />
<br />
The conditional entropies will then be<br />
<br />
<math>H(E|A) = - \sum_{i=1}^{|L_A|} p_i^a \sum_{j=1}^{|L_E|} p_{i,j}^{e|a} \log_2(p_{i,j}^{e|a})</math>, and<br />
<br />
<math>H(A|E) = - \sum_{j=1}^{|L_E|} p_j^e \sum_{i=1}^{|L_A|} p_{i,j}^{a|e} \log_2(p_{i,j}^{a|e})</math><br />
<br />
The final evaluation measures will then be the oversegmentation score<br />
<br />
<math>S_O = 1 - \frac{H(E|A)}{\log_2(|L_E|)}</math> , and the undersegmentation score<br />
<br />
<math>S_U = 1 - \frac{H(A|E)}{\log_2(|L_A|)}</math><br />
<br />
== Relevant Development Collections == <br />
*Jouni Paulus's [http://www.cs.tut.fi/sgn/arg/paulus/structure.html structure analysis page] links to a corpus of 177 Beatles songs ([http://www.cs.tut.fi/sgn/arg/paulus/beatles_sections_TUT.zip zip file]). The Beatles annotations are not a part of the TUTstructure07 dataset. That dataset contains 557 songs, a list of which is available [http://www.cs.tut.fi/sgn/arg/paulus/TUTstructure07_files.html here].<br />
<br />
*Ewald Peiszer's [http://www.ifs.tuwien.ac.at/mir/audiosegmentation.html thesis page] links to a portion of the corpus he used: 43 non-Beatles pop songs (including 10 J-pop songs) ([http://www.ifs.tuwien.ac.at/mir/audiosegmentation/dl/ep_groundtruth_excl_Paulus.zip zip file]).<br />
<br />
These public corpora give a combined 220 songs.<br />
<br />
== Time and hardware limits ==<br />
Due to the potentially high number of participants in this and other audio tasks, hard limits on the runtime of submissions will be imposed.<br />
<br />
A hard limit of 24 hours will be imposed on analysis times. Submissions exceeding this limit may not receive a result.<br />
<br />
<br />
== Potential Participants ==<br />
<br />
name / email<br />
Ju-Chiang Wang / ju-chiang.wang@bytedance.com<br />
<br />
== Discussion ==</div>Asriverhttps://www.music-ir.org/mirex/w/index.php?title=2010:MIREX_2010_Poster_List&diff=77092010:MIREX 2010 Poster List2010-08-04T09:05:11Z<p>Asriver: /* Add your author names here, once for each poster along with "title of some sort" and (Task(s) covered) */</p>
<hr />
<div>==MIREX 2010 Poster Session Planning List==<br />
The MIREX 2010 Poster Session will be held Wednesday, 11 August: 16:00 - 17:45. We will be holding the MIREX plenary meeting 13:00-14:00 as a working lunch on the same day.<br />
<br />
Our hosts in Utrecht need to know the number of posters so they can set up the room. Please add you name and the task(s) dealt with in your poster. <br />
<br />
We had many groups/individuals submit across tasks. You can choose to create one ISMIR poster bringing all your data together or can split up your data across, say, two or three posters. If you have questions, please contact me at jdownie@illinois.edu or the MIREX mailing list about task poster options.<br />
<br />
As a reminder, the MIREX posters need to follow the [http://ismir2010.ismir.net/information-for-authors/information-for-presenters/ ISMIR 2010 poster guidelines] (i.e., A0, portrait orientation).<br />
<br />
==Add your author names here, once for each poster along with "title of some sort" and (Task(s) covered)==<br />
# IMIRSEL: ''MIREX 2010 Overview, Part I'' (Train Test Tasks)<br />
# IMIRSEL: ''MIREX 2010 Overview, Part II'' (All Other Tasks)<br />
# Andreas Arzt and Gerhard Widmer: "Real-time Music Tracking using Tempo-aware On-line Dynamic Time Warping" (Real-time Audio to Score Alignment (a.k.a Score Following))<br />
# Pasi Saari and Olivier Lartillot: "SubEnsemble - Classification framework based on the Ensemble Approach and Feature Selection" (Train Test Tasks)<br />
# Gabriel Sargent, Frédéric Bimbot and Emmanuel Vincent: "Structural segmentation of songs using multi-criteria generalized likelihood ratio and regularity constraints" (Structural Segmentation Task)<br />
# Emmanouil Benetos and Simon Dixon: "Multiple fundamental frequency estimation using spectral structure and temporal evolution rules" (Multiple Fundamental Frequency Estimation & Tracking Task)<br />
# J. Urbano, J. Lloréns, J. Morato and S. Sánchez-Cuadrado: ''Local Alignment with Geometric Representations'' (Symbolic Melodic Similarity)<br />
# F.J.Rodriguez-Serrano, P.Vera-Candeas, P.Cabanas-Molero, J.J.Carabias-Orti, N.Ruiz-Reyes: ''AM Sinusoidal Modeling for Onset Detection FOR ONSET DETECTION'' (Audio Onset Detection)<br />
# R.Mata-Campos, F.J.Rodriguez-Serrano, P.Vera-Candeas, J.J.Carabias-Orti, F.J.Canadas-Quesada: ''Beat Tracking improved by AM Sinusoidal Modeled Onsets'' (Audio Beat Tracking)<br />
# F.J.Rodriguez-Serrano, P.Vera-Candeas, J.J.Carabias-Orti,P.Cabanas-Molero, N.Ruiz-Reyes: ''Real time audio to score alignment based on NLS multipitch estimation'' (Real-time Audio to Score Alignment (a.k.a Score Following))<br />
# F.J. Cañadas-Quesada, F. Rodríguez-Serrano, P. Vera-Candeas, N. Ruiz-Reyes and J. Carabias-Orti: ''Multiple Fundamental Frequency Estimation & Tracking in Polyphonic Music for MIREX 2010'' (Multiple Fundamental Frequency Estimation & Tracking)<br />
# Zhiyao Duan and Bryan Pardo: "A Real-time Score Follower for MIREX 2010" (Real-time Audio to Score Alignment (a.k.a Score Following))<br />
# Zhiyao Duan, Jinyu Han and Bryan Pardo: "A Multi-pitch Estimation and Tracking System" (Multiple Fundamental Frequency Estimation & Tracking Task)<br />
# I.S.H.Suyoto and A.L.Uitdenbogerd: "Orthogonal Pitch with IOI Symbolic Music Matching" (Symbolic Melodic Similarity)<br />
# J.-C. Wang, H.-Y. Lo, S.-K. Jeng and H.-M. Wang: "IISSLG Team: Audio Train/Test and Tag Classification for MIREX 2010" (Audio Train/Test Classification, Audio Tag Classification)<br />
# E. Di Buccio, N. Montecchio and N. Orio: "Applying Text-Based IR Techniques to Cover Song Identification" (Audio Cover Song Identification)<br />
# F. Eyben, B. Schuller: "MIREX 2010: Music Classification with the Munich openSMILE toolkit." (Audio Train/Test Tasks; Audio Tempo Estimation)<br />
# A. Gkiokas, V. Katsouros, G. Carayannis : "MIREX 2010 : Tempo Induction Using Filterbank Analysis and Tonal Features" (Audio Tempo Estimation)<br />
# Y. Zhu, H. Tan, L. Chaisorn: "Poster #1" on Audio Beat Tracking<br />
# H. Tan, Y. Zhu, L. Chaisorn: "Poster #2" on Audio Onset Detection<br />
<br />
==Below are some examples from MIREX 2009==<br />
<br />
# Matt Hoffman: ''Using CBA to Automatically Tag Songs'' (Audio tag classification/retrieval)<br />
# Suman Ravuri, Dan Ellis: ''The Hydra System of Cover Song Classification'' (Cover Song Identification)<br />
# Joan Serra, Massimiliano Zanin, Ralph G Andrzejak: ''Cover song retrieval by recurrence quantification and unsupervised set detection'' (Cover Song Identification)<br />
# MTG Team: "Music Type Groupers (MTG): Generic Music Classification Algorithms" (Audio Genre Classification, Mood Classification, Artist Identification, Classical Composer Identification)<br />
# R. Jang: "Poster #2" (placeholder to get the auto-counter to increment)<br />
# R. Jang: "Poster #3" (placeholder to get the auto-counter to increment)</div>Asriverhttps://www.music-ir.org/mirex/w/index.php?title=2010:MIREX_2010_Poster_List&diff=77082010:MIREX 2010 Poster List2010-08-04T09:04:35Z<p>Asriver: /* Add your author names here, once for each poster along with "title of some sort" and (Task(s) covered) */</p>
<hr />
<div>==MIREX 2010 Poster Session Planning List==<br />
The MIREX 2010 Poster Session will be held Wednesday, 11 August: 16:00 - 17:45. We will be holding the MIREX plenary meeting 13:00-14:00 as a working lunch on the same day.<br />
<br />
Our hosts in Utrecht need to know the number of posters so they can set up the room. Please add you name and the task(s) dealt with in your poster. <br />
<br />
We had many groups/individuals submit across tasks. You can choose to create one ISMIR poster bringing all your data together or can split up your data across, say, two or three posters. If you have questions, please contact me at jdownie@illinois.edu or the MIREX mailing list about task poster options.<br />
<br />
As a reminder, the MIREX posters need to follow the [http://ismir2010.ismir.net/information-for-authors/information-for-presenters/ ISMIR 2010 poster guidelines] (i.e., A0, portrait orientation).<br />
<br />
==Add your author names here, once for each poster along with "title of some sort" and (Task(s) covered)==<br />
# IMIRSEL: ''MIREX 2010 Overview, Part I'' (Train Test Tasks)<br />
# IMIRSEL: ''MIREX 2010 Overview, Part II'' (All Other Tasks)<br />
# Andreas Arzt and Gerhard Widmer: "Real-time Music Tracking using Tempo-aware On-line Dynamic Time Warping" (Real-time Audio to Score Alignment (a.k.a Score Following))<br />
# Pasi Saari and Olivier Lartillot: "SubEnsemble - Classification framework based on the Ensemble Approach and Feature Selection" (Train Test Tasks)<br />
# Gabriel Sargent, Frédéric Bimbot and Emmanuel Vincent: "Structural segmentation of songs using multi-criteria generalized likelihood ratio and regularity constraints" (Structural Segmentation Task)<br />
# Emmanouil Benetos and Simon Dixon: "Multiple fundamental frequency estimation using spectral structure and temporal evolution rules" (Multiple Fundamental Frequency Estimation & Tracking Task)<br />
# J. Urbano, J. Lloréns, J. Morato and S. Sánchez-Cuadrado: ''Local Alignment with Geometric Representations'' (Symbolic Melodic Similarity)<br />
# F.J.Rodriguez-Serrano, P.Vera-Candeas, P.Cabanas-Molero, J.J.Carabias-Orti, N.Ruiz-Reyes: ''AM Sinusoidal Modeling for Onset Detection FOR ONSET DETECTION'' (Audio Onset Detection)<br />
# R.Mata-Campos, F.J.Rodriguez-Serrano, P.Vera-Candeas, J.J.Carabias-Orti, F.J.Canadas-Quesada: ''Beat Tracking improved by AM Sinusoidal Modeled Onsets'' (Audio Beat Tracking)<br />
# F.J.Rodriguez-Serrano, P.Vera-Candeas, J.J.Carabias-Orti,P.Cabanas-Molero, N.Ruiz-Reyes: ''Real time audio to score alignment based on NLS multipitch estimation'' (Real-time Audio to Score Alignment (a.k.a Score Following))<br />
# F.J. Cañadas-Quesada, F. Rodríguez-Serrano, P. Vera-Candeas, N. Ruiz-Reyes and J. Carabias-Orti: ''Multiple Fundamental Frequency Estimation & Tracking in Polyphonic Music for MIREX 2010'' (Multiple Fundamental Frequency Estimation & Tracking)<br />
# Zhiyao Duan and Bryan Pardo: "A Real-time Score Follower for MIREX 2010" (Real-time Audio to Score Alignment (a.k.a Score Following))<br />
# Zhiyao Duan, Jinyu Han and Bryan Pardo: "A Multi-pitch Estimation and Tracking System" (Multiple Fundamental Frequency Estimation & Tracking Task)<br />
# I.S.H.Suyoto and A.L.Uitdenbogerd: "Orthogonal Pitch with IOI Symbolic Music Matching" (Symbolic Melodic Similarity)<br />
# J.-C. Wang, H.-Y. Lo, S.-K. Jeng and H.-M. Wang: "IISSLG Team: Audio Train/Test and Tag Classification fo MIREX 2010" (Audio Train/Test Classification, Audio Tag Classification)<br />
# E. Di Buccio, N. Montecchio and N. Orio: "Applying Text-Based IR Techniques to Cover Song Identification" (Audio Cover Song Identification)<br />
# F. Eyben, B. Schuller: "MIREX 2010: Music Classification with the Munich openSMILE toolkit." (Audio Train/Test Tasks; Audio Tempo Estimation)<br />
# A. Gkiokas, V. Katsouros, G. Carayannis : "MIREX 2010 : Tempo Induction Using Filterbank Analysis and Tonal Features" (Audio Tempo Estimation)<br />
# Y. Zhu, H. Tan, L. Chaisorn: "Poster #1" on Audio Beat Tracking<br />
# H. Tan, Y. Zhu, L. Chaisorn: "Poster #2" on Audio Onset Detection<br />
<br />
==Below are some examples from MIREX 2009==<br />
<br />
# Matt Hoffman: ''Using CBA to Automatically Tag Songs'' (Audio tag classification/retrieval)<br />
# Suman Ravuri, Dan Ellis: ''The Hydra System of Cover Song Classification'' (Cover Song Identification)<br />
# Joan Serra, Massimiliano Zanin, Ralph G Andrzejak: ''Cover song retrieval by recurrence quantification and unsupervised set detection'' (Cover Song Identification)<br />
# MTG Team: "Music Type Groupers (MTG): Generic Music Classification Algorithms" (Audio Genre Classification, Mood Classification, Artist Identification, Classical Composer Identification)<br />
# R. Jang: "Poster #2" (placeholder to get the auto-counter to increment)<br />
# R. Jang: "Poster #3" (placeholder to get the auto-counter to increment)</div>Asriverhttps://www.music-ir.org/mirex/w/index.php?title=2010:MIREX_2010_Poster_List&diff=77072010:MIREX 2010 Poster List2010-08-04T09:04:00Z<p>Asriver: /* Add your author names here, once for each poster along with "title of some sort" and (Task(s) covered) */</p>
<hr />
<div>==MIREX 2010 Poster Session Planning List==<br />
The MIREX 2010 Poster Session will be held Wednesday, 11 August: 16:00 - 17:45. We will be holding the MIREX plenary meeting 13:00-14:00 as a working lunch on the same day.<br />
<br />
Our hosts in Utrecht need to know the number of posters so they can set up the room. Please add you name and the task(s) dealt with in your poster. <br />
<br />
We had many groups/individuals submit across tasks. You can choose to create one ISMIR poster bringing all your data together or can split up your data across, say, two or three posters. If you have questions, please contact me at jdownie@illinois.edu or the MIREX mailing list about task poster options.<br />
<br />
As a reminder, the MIREX posters need to follow the [http://ismir2010.ismir.net/information-for-authors/information-for-presenters/ ISMIR 2010 poster guidelines] (i.e., A0, portrait orientation).<br />
<br />
==Add your author names here, once for each poster along with "title of some sort" and (Task(s) covered)==<br />
# IMIRSEL: ''MIREX 2010 Overview, Part I'' (Train Test Tasks)<br />
# IMIRSEL: ''MIREX 2010 Overview, Part II'' (All Other Tasks)<br />
# Andreas Arzt and Gerhard Widmer: "Real-time Music Tracking using Tempo-aware On-line Dynamic Time Warping" (Real-time Audio to Score Alignment (a.k.a Score Following))<br />
# Pasi Saari and Olivier Lartillot: "SubEnsemble - Classification framework based on the Ensemble Approach and Feature Selection" (Train Test Tasks)<br />
# Gabriel Sargent, Frédéric Bimbot and Emmanuel Vincent: "Structural segmentation of songs using multi-criteria generalized likelihood ratio and regularity constraints" (Structural Segmentation Task)<br />
# Emmanouil Benetos and Simon Dixon: "Multiple fundamental frequency estimation using spectral structure and temporal evolution rules" (Multiple Fundamental Frequency Estimation & Tracking Task)<br />
# J. Urbano, J. Lloréns, J. Morato and S. Sánchez-Cuadrado: ''Local Alignment with Geometric Representations'' (Symbolic Melodic Similarity)<br />
# F.J.Rodriguez-Serrano, P.Vera-Candeas, P.Cabanas-Molero, J.J.Carabias-Orti, N.Ruiz-Reyes: ''AM Sinusoidal Modeling for Onset Detection FOR ONSET DETECTION'' (Audio Onset Detection)<br />
# R.Mata-Campos, F.J.Rodriguez-Serrano, P.Vera-Candeas, J.J.Carabias-Orti, F.J.Canadas-Quesada: ''Beat Tracking improved by AM Sinusoidal Modeled Onsets'' (Audio Beat Tracking)<br />
# F.J.Rodriguez-Serrano, P.Vera-Candeas, J.J.Carabias-Orti,P.Cabanas-Molero, N.Ruiz-Reyes: ''Real time audio to score alignment based on NLS multipitch estimation'' (Real-time Audio to Score Alignment (a.k.a Score Following))<br />
# F.J. Cañadas-Quesada, F. Rodríguez-Serrano, P. Vera-Candeas, N. Ruiz-Reyes and J. Carabias-Orti: ''Multiple Fundamental Frequency Estimation & Tracking in Polyphonic Music for MIREX 2010'' (Multiple Fundamental Frequency Estimation & Tracking)<br />
# Zhiyao Duan and Bryan Pardo: "A Real-time Score Follower for MIREX 2010" (Real-time Audio to Score Alignment (a.k.a Score Following))<br />
# Zhiyao Duan, Jinyu Han and Bryan Pardo: "A Multi-pitch Estimation and Tracking System" (Multiple Fundamental Frequency Estimation & Tracking Task)<br />
# I.S.H.Suyoto and A.L.Uitdenbogerd: "Orthogonal Pitch with IOI Symbolic Music Matching" (Symbolic Melodic Similarity)<br />
# J.-C. Wang, H.-Y. Lo, S.-K. Jeng and H.-M. Wang: "IISSLG Team: Audio Train/Test and Tag Classification fo MIREX 2010" (Audio Train/Test Classification Tasks, Audio Tag Classification)<br />
# E. Di Buccio, N. Montecchio and N. Orio: "Applying Text-Based IR Techniques to Cover Song Identification" (Audio Cover Song Identification)<br />
# F. Eyben, B. Schuller: "MIREX 2010: Music Classification with the Munich openSMILE toolkit." (Audio Train/Test Tasks; Audio Tempo Estimation)<br />
# A. Gkiokas, V. Katsouros, G. Carayannis : "MIREX 2010 : Tempo Induction Using Filterbank Analysis and Tonal Features" (Audio Tempo Estimation)<br />
# Y. Zhu, H. Tan, L. Chaisorn: "Poster #1" on Audio Beat Tracking<br />
# H. Tan, Y. Zhu, L. Chaisorn: "Poster #2" on Audio Onset Detection<br />
<br />
==Below are some examples from MIREX 2009==<br />
<br />
# Matt Hoffman: ''Using CBA to Automatically Tag Songs'' (Audio tag classification/retrieval)<br />
# Suman Ravuri, Dan Ellis: ''The Hydra System of Cover Song Classification'' (Cover Song Identification)<br />
# Joan Serra, Massimiliano Zanin, Ralph G Andrzejak: ''Cover song retrieval by recurrence quantification and unsupervised set detection'' (Cover Song Identification)<br />
# MTG Team: "Music Type Groupers (MTG): Generic Music Classification Algorithms" (Audio Genre Classification, Mood Classification, Artist Identification, Classical Composer Identification)<br />
# R. Jang: "Poster #2" (placeholder to get the auto-counter to increment)<br />
# R. Jang: "Poster #3" (placeholder to get the auto-counter to increment)</div>Asriverhttps://www.music-ir.org/mirex/w/index.php?title=2010:MIREX_2010_Poster_List&diff=75792010:MIREX 2010 Poster List2010-08-02T05:49:23Z<p>Asriver: /* Add your author names here, once for each poster along with "title of some sort" and (Task(s) covered) */</p>
<hr />
<div>==MIREX 2010 Poster Session Planning List==<br />
The MIREX 2010 Poster Session will be held Wednesday, 11 August: 16:00 - 17:45. We will be holding the MIREX plenary meeting 13:00-14:00 as a working lunch on the same day.<br />
<br />
Our hosts in Utrecht need to know the number of posters so they can set up the room. Please add you name and the task(s) dealt with in your poster. <br />
<br />
We had many groups/individuals submit across tasks. You can choose to create one ISMIR poster bringing all your data together or can split up your data across, say, two or three posters. If you have questions, please contact me at jdownie@illinois.edu or the MIREX mailing list about task poster options.<br />
<br />
As a reminder, the MIREX posters need to follow the [http://ismir2010.ismir.net/information-for-authors/information-for-presenters/ ISMIR 2010 poster guidelines] (i.e., A0, portrait orientation).<br />
<br />
==Add your author names here, once for each poster along with "title of some sort" and (Task(s) covered)==<br />
# IMIRSEL: ''MIREX 2010 Overview, Part I'' (Train Test Tasks)<br />
# IMIRSEL: ''MIREX 2010 Overview, Part II'' (All Other Tasks)<br />
# Andreas Arzt and Gerhard Widmer: "Real-time Music Tracking using Tempo-aware On-line Dynamic Time Warping" (Real-time Audio to Score Alignment (a.k.a Score Following))<br />
# Pasi Saari and Olivier Lartillot: "SubEnsemble - Classification framework based on the Ensemble Approach and Feature Selection" (Train Test Tasks)<br />
# Gabriel Sargent, Frédéric Bimbot and Emmanuel Vincent: "Structural segmentation of songs using multi-criteria generalized likelihood ratio and regularity constraints" (Structural Segmentation Task)<br />
# Emmanouil Benetos and Simon Dixon: "Multiple fundamental frequency estimation using spectral structure and temporal evolution rules" (Multiple Fundamental Frequency Estimation & Tracking Task)<br />
# J. Urbano, J. Lloréns, J. Morato and S. Sánchez-Cuadrado: ''Local Alignment with Geometric Representations'' (Symbolic Melodic Similarity)<br />
# F.J.Rodriguez-Serrano, P.Vera-Candeas, P.Cabanas-Molero, J.J.Carabias-Orti, N.Ruiz-Reyes: ''AM Sinusoidal Modeling for Onset Detection FOR ONSET DETECTION'' (Audio Onset Detection)<br />
# R.Mata-Campos, F.J.Rodriguez-Serrano, P.Vera-Candeas, J.J.Carabias-Orti, F.J.Canadas-Quesada: ''Beat Tracking improved by AM Sinusoidal Modeled Onsets'' (Audio Beat Tracking)<br />
# F.J.Rodriguez-Serrano, P.Vera-Candeas, J.J.Carabias-Orti,P.Cabanas-Molero, N.Ruiz-Reyes: ''Real time audio to score alignment based on NLS multipitch estimation'' (Real-time Audio to Score Alignment (a.k.a Score Following))<br />
# F.J. Cañadas-Quesada, F. Rodríguez-Serrano, P. Vera-Candeas, N. Ruiz-Reyes and J. Carabias-Orti: ''Multiple Fundamental Frequency Estimation & Tracking in Polyphonic Music for MIREX 2010'' (Multiple Fundamental Frequency Estimation & Tracking)<br />
# Zhiyao Duan and Bryan Pardo: "A Real-time Score Follower for MIREX 2010" (Real-time Audio to Score Alignment (a.k.a Score Following))<br />
# Zhiyao Duan, Jinyu Han and Bryan Pardo: "A Multi-pitch Estimation and Tracking System" (Multiple Fundamental Frequency Estimation & Tracking Task)<br />
# I.S.H.Suyoto and A.L.Uitdenbogerd: "Orthogonal Pitch with IOI Symbolic Music Matching" (Symbolic Melodic Similarity)<br />
# J.-C. Wang, H.-Y. Lo, S.-K. Jeng and H.-M. Wang: "MIREX 2010: Audio Train/Test Classification by Semantic Transformation and Classifier Ensemble" (Audio Train/Test Classification Tasks)<br />
# J.-C. Wang, H.-Y. Lo, and H.-M. Wang: "MIREX 2010: Improvements for Audio Tag Classification" (Audio Tag Classification)<br />
# Emanuele Di Buccio, Nicola Montecchio, Nicola Orio: "Applying Text-Based IR Techniques to Cover Song Identification" (Audio Cover Song Identification)<br />
<br />
==Below are some examples from MIREX 2009==<br />
<br />
# Matt Hoffman: ''Using CBA to Automatically Tag Songs'' (Audio tag classification/retrieval)<br />
# Suman Ravuri, Dan Ellis: ''The Hydra System of Cover Song Classification'' (Cover Song Identification)<br />
# Joan Serra, Massimiliano Zanin, Ralph G Andrzejak: ''Cover song retrieval by recurrence quantification and unsupervised set detection'' (Cover Song Identification)<br />
# MTG Team: "Music Type Groupers (MTG): Generic Music Classification Algorithms" (Audio Genre Classification, Mood Classification, Artist Identification, Classical Composer Identification)<br />
# R. Jang: "Poster #2" (placeholder to get the auto-counter to increment)<br />
# R. Jang: "Poster #3" (placeholder to get the auto-counter to increment)</div>Asriverhttps://www.music-ir.org/mirex/w/index.php?title=2010:MIREX_2010_Poster_List&diff=75772010:MIREX 2010 Poster List2010-08-02T05:48:09Z<p>Asriver: /* Add your author names here, once for each poster along with "title of some sort" and (Task(s) covered) */</p>
<hr />
<div>==MIREX 2010 Poster Session Planning List==<br />
The MIREX 2010 Poster Session will be held Wednesday, 11 August: 16:00 - 17:45. We will be holding the MIREX plenary meeting 13:00-14:00 as a working lunch on the same day.<br />
<br />
Our hosts in Utrecht need to know the number of posters so they can set up the room. Please add you name and the task(s) dealt with in your poster. <br />
<br />
We had many groups/individuals submit across tasks. You can choose to create one ISMIR poster bringing all your data together or can split up your data across, say, two or three posters. If you have questions, please contact me at jdownie@illinois.edu or the MIREX mailing list about task poster options.<br />
<br />
As a reminder, the MIREX posters need to follow the [http://ismir2010.ismir.net/information-for-authors/information-for-presenters/ ISMIR 2010 poster guidelines] (i.e., A0, portrait orientation).<br />
<br />
==Add your author names here, once for each poster along with "title of some sort" and (Task(s) covered)==<br />
# IMIRSEL: ''MIREX 2010 Overview, Part I'' (Train Test Tasks)<br />
# IMIRSEL: ''MIREX 2010 Overview, Part II'' (All Other Tasks)<br />
# Andreas Arzt and Gerhard Widmer: "Real-time Music Tracking using Tempo-aware On-line Dynamic Time Warping" (Real-time Audio to Score Alignment (a.k.a Score Following))<br />
# Pasi Saari and Olivier Lartillot: "SubEnsemble - Classification framework based on the Ensemble Approach and Feature Selection" (Train Test Tasks)<br />
# Gabriel Sargent, Frédéric Bimbot and Emmanuel Vincent: "Structural segmentation of songs using multi-criteria generalized likelihood ratio and regularity constraints" (Structural Segmentation Task)<br />
# Emmanouil Benetos and Simon Dixon: "Multiple fundamental frequency estimation using spectral structure and temporal evolution rules" (Multiple Fundamental Frequency Estimation & Tracking Task)<br />
# J. Urbano, J. Lloréns, J. Morato and S. Sánchez-Cuadrado: ''Local Alignment with Geometric Representations'' (Symbolic Melodic Similarity)<br />
# F.J.Rodriguez-Serrano, P.Vera-Candeas, P.Cabanas-Molero, J.J.Carabias-Orti, N.Ruiz-Reyes: ''AM Sinusoidal Modeling for Onset Detection FOR ONSET DETECTION'' (Audio Onset Detection)<br />
# R.Mata-Campos, F.J.Rodriguez-Serrano, P.Vera-Candeas, J.J.Carabias-Orti, F.J.Canadas-Quesada: ''Beat Tracking improved by AM Sinusoidal Modeled Onsets'' (Audio Beat Tracking)<br />
# F.J.Rodriguez-Serrano, P.Vera-Candeas, J.J.Carabias-Orti,P.Cabanas-Molero, N.Ruiz-Reyes: ''Real time audio to score alignment based on NLS multipitch estimation'' (Real-time Audio to Score Alignment (a.k.a Score Following))<br />
# F.J. Cañadas-Quesada, F. Rodríguez-Serrano, P. Vera-Candeas, N. Ruiz-Reyes and J. Carabias-Orti: ''Multiple Fundamental Frequency Estimation & Tracking in Polyphonic Music for MIREX 2010'' (Multiple Fundamental Frequency Estimation & Tracking)<br />
# Zhiyao Duan and Bryan Pardo: "A Real-time Score Follower for MIREX 2010" (Real-time Audio to Score Alignment (a.k.a Score Following))<br />
# Zhiyao Duan, Jinyu Han and Bryan Pardo: "A Multi-pitch Estimation and Tracking System" (Multiple Fundamental Frequency Estimation & Tracking Task)<br />
# I.S.H.Suyoto and A.L.Uitdenbogerd: "Orthogonal Pitch with IOI Symbolic Music Matching" (Symbolic Melodic Similarity)<br />
# J.-C. Wang, H.-Y. Lo, S.-K. Jeng and H.-M.Wang: "MIREX 2010: Audio Train/Test Classification by Semantic Transformation and Classifier Ensemble" (Audio Train/Test Classification Tasks)<br />
# J.-C. Wang, H.-Y. Lo, and H.-M.Wang: "MIREX 2010: Improvements for Audio Tag Classification" (Audio Tag Classification)<br />
<br />
==Below are some examples from MIREX 2009==<br />
<br />
# Matt Hoffman: ''Using CBA to Automatically Tag Songs'' (Audio tag classification/retrieval)<br />
# Suman Ravuri, Dan Ellis: ''The Hydra System of Cover Song Classification'' (Cover Song Identification)<br />
# Joan Serra, Massimiliano Zanin, Ralph G Andrzejak: ''Cover song retrieval by recurrence quantification and unsupervised set detection'' (Cover Song Identification)<br />
# MTG Team: "Music Type Groupers (MTG): Generic Music Classification Algorithms" (Audio Genre Classification, Mood Classification, Artist Identification, Classical Composer Identification)<br />
# R. Jang: "Poster #2" (placeholder to get the auto-counter to increment)<br />
# R. Jang: "Poster #3" (placeholder to get the auto-counter to increment)</div>Asriverhttps://www.music-ir.org/mirex/w/index.php?title=2010:MIREX_2010_Poster_List&diff=75762010:MIREX 2010 Poster List2010-08-02T05:46:53Z<p>Asriver: /* Add your author names here, once for each poster along with "title of some sort" and (Task(s) covered) */</p>
<hr />
<div>==MIREX 2010 Poster Session Planning List==<br />
The MIREX 2010 Poster Session will be held Wednesday, 11 August: 16:00 - 17:45. We will be holding the MIREX plenary meeting 13:00-14:00 as a working lunch on the same day.<br />
<br />
Our hosts in Utrecht need to know the number of posters so they can set up the room. Please add you name and the task(s) dealt with in your poster. <br />
<br />
We had many groups/individuals submit across tasks. You can choose to create one ISMIR poster bringing all your data together or can split up your data across, say, two or three posters. If you have questions, please contact me at jdownie@illinois.edu or the MIREX mailing list about task poster options.<br />
<br />
As a reminder, the MIREX posters need to follow the [http://ismir2010.ismir.net/information-for-authors/information-for-presenters/ ISMIR 2010 poster guidelines] (i.e., A0, portrait orientation).<br />
<br />
==Add your author names here, once for each poster along with "title of some sort" and (Task(s) covered)==<br />
# IMIRSEL: ''MIREX 2010 Overview, Part I'' (Train Test Tasks)<br />
# IMIRSEL: ''MIREX 2010 Overview, Part II'' (All Other Tasks)<br />
# Andreas Arzt and Gerhard Widmer: "Real-time Music Tracking using Tempo-aware On-line Dynamic Time Warping" (Real-time Audio to Score Alignment (a.k.a Score Following))<br />
# Pasi Saari and Olivier Lartillot: "SubEnsemble - Classification framework based on the Ensemble Approach and Feature Selection" (Train Test Tasks)<br />
# Gabriel Sargent, Frédéric Bimbot and Emmanuel Vincent: "Structural segmentation of songs using multi-criteria generalized likelihood ratio and regularity constraints" (Structural Segmentation Task)<br />
# Emmanouil Benetos and Simon Dixon: "Multiple fundamental frequency estimation using spectral structure and temporal evolution rules" (Multiple Fundamental Frequency Estimation & Tracking Task)<br />
# J. Urbano, J. Lloréns, J. Morato and S. Sánchez-Cuadrado: ''Local Alignment with Geometric Representations'' (Symbolic Melodic Similarity)<br />
# F.J.Rodriguez-Serrano, P.Vera-Candeas, P.Cabanas-Molero, J.J.Carabias-Orti, N.Ruiz-Reyes: ''AM Sinusoidal Modeling for Onset Detection FOR ONSET DETECTION'' (Audio Onset Detection)<br />
# R.Mata-Campos, F.J.Rodriguez-Serrano, P.Vera-Candeas, J.J.Carabias-Orti, F.J.Canadas-Quesada: ''Beat Tracking improved by AM Sinusoidal Modeled Onsets'' (Audio Beat Tracking)<br />
# F.J.Rodriguez-Serrano, P.Vera-Candeas, J.J.Carabias-Orti,P.Cabanas-Molero, N.Ruiz-Reyes: ''Real time audio to score alignment based on NLS multipitch estimation'' (Real-time Audio to Score Alignment (a.k.a Score Following))<br />
# F.J. Cañadas-Quesada, F. Rodríguez-Serrano, P. Vera-Candeas, N. Ruiz-Reyes and J. Carabias-Orti: ''Multiple Fundamental Frequency Estimation & Tracking in Polyphonic Music for MIREX 2010'' (Multiple Fundamental Frequency Estimation & Tracking)<br />
# Zhiyao Duan and Bryan Pardo: "A Real-time Score Follower for MIREX 2010" (Real-time Audio to Score Alignment (a.k.a Score Following))<br />
# Zhiyao Duan, Jinyu Han and Bryan Pardo: "A Multi-pitch Estimation and Tracking System" (Multiple Fundamental Frequency Estimation & Tracking Task)<br />
# I.S.H.Suyoto and A.L.Uitdenbogerd: "Orthogonal Pitch with IOI Symbolic Music Matching" (Symbolic Melodic Similarity)<br />
# J.-C. Wang, H.-Y. Lo, S.-K. Jeng and H.-M.Wang: "MIREX 2010: Audio Train/Test Classification by Semantic Transformation and Classifier Ensemble" (Audio Train/Test Classification Tasks)<br />
# J.-C. Wang, H.-Y. Lo, and H.-M.Wang: "MIREX 2010: Improvements of Audio Tag Classification" (Audio Tag Classification)<br />
<br />
==Below are some examples from MIREX 2009==<br />
<br />
# Matt Hoffman: ''Using CBA to Automatically Tag Songs'' (Audio tag classification/retrieval)<br />
# Suman Ravuri, Dan Ellis: ''The Hydra System of Cover Song Classification'' (Cover Song Identification)<br />
# Joan Serra, Massimiliano Zanin, Ralph G Andrzejak: ''Cover song retrieval by recurrence quantification and unsupervised set detection'' (Cover Song Identification)<br />
# MTG Team: "Music Type Groupers (MTG): Generic Music Classification Algorithms" (Audio Genre Classification, Mood Classification, Artist Identification, Classical Composer Identification)<br />
# R. Jang: "Poster #2" (placeholder to get the auto-counter to increment)<br />
# R. Jang: "Poster #3" (placeholder to get the auto-counter to increment)</div>Asriver