Difference between revisions of "2019:Audio Chord Estimation Results"

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==Introduction==
 
==Introduction==
  
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This page contains the results of the 2019 edition of the MIREX automatic chord estimation tasks. This edition was the sixth one since the reorganization of the evaluation procedure in 2013. The results can therefore be directly compared to those of the last five years. Chord labels are evaluated according to five different chord vocabularies and the segmentation is also assessed. Additional information about the used measures can be found on the page of the [[2013:Audio_Chord_Estimation_Results_MIREX_2009 | 2013 edition]].  
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This page contains the results of the 2019 edition of the MIREX automatic chord estimation tasks. This edition was the seventh one since the reorganization of the evaluation procedure in 2013. The results can therefore be directly compared to those of the last six years. Chord labels are evaluated according to five different chord vocabularies and the segmentation is also assessed. Additional information about the used measures can be found on the page of the [[2013:Audio_Chord_Estimation_Results_MIREX_2009 | 2013 edition]].  
  
 
==What’s new?==
 
==What’s new?==
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==Software==
 
==Software==
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All software used for the evaluation has been made open-source. The evaluation framework is described by [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6637748 Pauwels and Peeters (2013)]. The corresponding binaries and code repository can be found on [https://github.com/jpauwels/MusOOEvaluator GitHub] and the used measures are available as presets. The raw algorithmic output will be provided later <!--provided below makes it possible to calculate the additional measures from the paper (separate results for tetrads, etc.), in addition to those presented below.--> More help can be found in the [https://github.com/jpauwels/MusOOEvaluator/blob/master/README.md readme].
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All software used for the evaluation has been made open-source. The evaluation framework is described by [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6637748 Pauwels and Peeters (2013)]. The corresponding binaries and code repository can be found on [https://github.com/jpauwels/MusOOEvaluator GitHub] and the used measures are available as presets. The raw algorithmic output provided below makes it possible to calculate the additional measures from the paper (separate results for tetrads, etc.), in addition to those presented below. More help can be found in the [https://github.com/jpauwels/MusOOEvaluator/blob/master/README.md readme].
  
 
The statistical comparison between the different submissions is explained in [http://www.terasoft.com.tw/conf/ismir2014/proceedings/T095_250_Paper.pdf Burgoyne et al. (2014)]. The software is available at [https://bitbucket.org/jaburgoyne/mirexace BitBucket]. It uses the detailed results provided below as input.
 
The statistical comparison between the different submissions is explained in [http://www.terasoft.com.tw/conf/ismir2014/proceedings/T095_250_Paper.pdf Burgoyne et al. (2014)]. The software is available at [https://bitbucket.org/jaburgoyne/mirexace BitBucket]. It uses the detailed results provided below as input.

Latest revision as of 04:27, 14 October 2020

Introduction

This page contains the results of the 2019 edition of the MIREX automatic chord estimation tasks. This edition was the seventh one since the reorganization of the evaluation procedure in 2013. The results can therefore be directly compared to those of the last six years. Chord labels are evaluated according to five different chord vocabularies and the segmentation is also assessed. Additional information about the used measures can be found on the page of the 2013 edition.

What’s new?

  • All datasets and evaluation procedures are the same as last year's MIREX.

Software

All software used for the evaluation has been made open-source. The evaluation framework is described by Pauwels and Peeters (2013). The corresponding binaries and code repository can be found on GitHub and the used measures are available as presets. The raw algorithmic output provided below makes it possible to calculate the additional measures from the paper (separate results for tetrads, etc.), in addition to those presented below. More help can be found in the readme.

The statistical comparison between the different submissions is explained in Burgoyne et al. (2014). The software is available at BitBucket. It uses the detailed results provided below as input.

Submissions

Abstract Contributors
CLSYJ1 PDF i Chien, Song Rong Lee, Yeh Ssuhung, Tzu-Chun Yeh, Jyh-Shing Roger Jang
CM1 PDF Chris Cannam, Matthias Mauch

Results

Summary

All figures can be interpreted as percentages and range from 0 (worst) to 100 (best).

Isophonics2009
Algorithm MirexRoot MirexMajMin MirexMajMinBass MirexSevenths MirexSeventhsBass MeanSeg UnderSeg OverSeg
CLSYJ1 79.89 76.50 74.33 67.79 65.82 82.61 81.56 85.11
CM1 78.66 75.51 72.58 54.78 52.36 85.87 87.22 85.98

download these results as csv

Billboard2012
Algorithm MirexRoot MirexMajMin MirexMajMinBass MirexSevenths MirexSeventhsBass MeanSeg UnderSeg OverSeg
CLSYJ1 76.32 73.38 71.60 63.59 61.92 82.05 81.74 83.77
CM1 74.23 72.31 70.25 55.44 53.48 83.60 85.33 83.31

download these results as csv

Billboard2013
Algorithm MirexRoot MirexMajMin MirexMajMinBass MirexSevenths MirexSeventhsBass MeanSeg UnderSeg OverSeg
CLSYJ1 73.69 69.47 67.66 57.41 55.78 78.80 78.67 82.52
CM1 71.23 67.36 65.28 49.07 47.25 81.50 83.13 82.53

download these results as csv

JayChou29
Algorithm MirexRoot MirexMajMin MirexMajMinBass MirexSevenths MirexSeventhsBass MeanSeg UnderSeg OverSeg
CLSYJ1 65.44 62.49 55.27 46.83 40.60 78.95 80.78 77.55
CM1 72.82 72.15 65.55 54.46 49.05 86.58 86.94 86.84

download these results as csv

RobbieWilliams
Algorithm MirexRoot MirexMajMin MirexMajMinBass MirexSevenths MirexSeventhsBass MeanSeg UnderSeg OverSeg
CLSYJ1 79.89 75.45 73.73 68.57 66.91 83.60 83.72 84.32
CM1 81.94 78.29 76.09 57.97 55.94 87.95 89.00 87.39

download these results as csv

RWC-Popular
Algorithm MirexRoot MirexMajMin MirexMajMinBass MirexSevenths MirexSeventhsBass MeanSeg UnderSeg OverSeg
CLSYJ1 70.33 66.77 63.42 56.09 52.97 79.26 79.17 80.14
CM1 79.12 77.95 74.30 63.33 59.89 88.62 88.11 89.67

download these results as csv

USPOP2002Chords
Algorithm MirexRoot MirexMajMin MirexMajMinBass MirexSevenths MirexSeventhsBass MeanSeg UnderSeg OverSeg
CLSYJ1 76.23 72.30 68.75 62.27 58.92 82.13 82.12 83.24
CM1 78.41 76.24 72.74 59.84 56.67 85.81 86.81 86.02

download these results as csv

CASD-Annotator1
Algorithm MirexRoot MirexMajMin MirexMajMinBass MirexSevenths MirexSeventhsBass MeanSeg UnderSeg OverSeg
CLSYJ1 69.95 65.12 63.49 57.03 55.56 79.68 80.43 81.67
CM1 72.55 69.00 66.59 49.76 47.92 83.99 85.95 83.96

download these results as csv

CASD-Annotator2
Algorithm MirexRoot MirexMajMin MirexMajMinBass MirexSevenths MirexSeventhsBass MeanSeg UnderSeg OverSeg
CLSYJ1 71.17 65.38 63.93 54.83 53.41 76.74 75.15 82.82
CM1 72.26 68.17 66.44 44.62 43.29 80.23 79.71 84.64

download these results as csv

CASD-Annotator3
Algorithm MirexRoot MirexMajMin MirexMajMinBass MirexSevenths MirexSeventhsBass MeanSeg UnderSeg OverSeg
CLSYJ1 69.53 65.07 62.72 54.46 52.30 76.78 74.19 83.03
CM1 73.08 69.29 66.28 50.88 48.46 81.49 79.69 86.08

download these results as csv

CASD-Annotator4
Algorithm MirexRoot MirexMajMin MirexMajMinBass MirexSevenths MirexSeventhsBass MeanSeg UnderSeg OverSeg
CLSYJ1 62.26 57.15 48.90 48.74 41.38 77.19 79.02 77.69
CM1 61.55 57.37 48.05 38.28 32.15 79.53 82.70 78.29

download these results as csv


Detailed Results

More details about the performance of the algorithms, including per-song performance and supplementary statistics, are available from this repository. .

Algorithmic Output

The raw output of the algorithms are available in this repository. They can be used to experiment with alternative evaluation measures and statistics.

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