2008:Audio Music Mood Classification Results

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Contents

Introduction

These are the results for the 2008 running of the Audio Music Mood Classification task. For background information about this task set please refer to the 2008:Audio Music Mood Classification page.

General Legend

Team ID

GP1 = G. Peeters
GT1 = G. Tzanetakis
GT2 = G. Tzanetakis
GT3 = G. Tzanetakis
HW = H. Wang
KL = K. Lee
LRPPI1 = T. Lidy, A. Rauber, A. Pertusa, P. Peonce de León, J. M. Iñesta 1
LRPPI2 = T. Lidy, A. Rauber, A. Pertusa, P. Peonce de León, J. M. Iñesta 2
LRPPI3 = T. Lidy, A. Rauber, A. Pertusa, P. Peonce de León, J. M. Iñesta 3
LRPPI4 = T. Lidy, A. Rauber, A. Pertusa, P. Peonce de León, J. M. Iñesta 4
ME1 = M. I. Mandel, D. P. W. Ellis 1
ME2 = M. I. Mandel, D. P. W. Ellis 2
ME3 = M. I. Mandel, D. P. W. Ellis 3

Overall Summary Results

MIREX 2008 Audio Mood Classification Summary Results - Raw Classification Accuracy Averaged Over Three Train/Test Folds

Participant Average Classifcation Accuracy
GP1 63.67%
GT1 55.00%
GT2 52.50%
GT3 58.20%
HW 30.33%
KL 49.83%
LRPPI1 56.00%
LRPPI2 55.50%
LRPPI3 54.50%
LRPPI4 55.50%
ME1 50.33%
ME2 50.00%
ME3 49.67%

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Accuracy Across Folds
Classification fold GP1 GT1 GT2 GT3 HW KL LRPPI1 LRPPI2 LRPPI3 LRPPI4 ME1 ME2 ME3
0 0.715 0.630 0.565 0.679 0.365 0.515 0.660 0.610 0.635 0.625 0.545 0.540 0.535
1 0.610 0.550 0.535 0.549 0.315 0.520 0.480 0.505 0.485 0.510 0.510 0.505 0.510
2 0.585 0.470 0.475 0.518 0.230 0.460 0.540 0.550 0.515 0.530 0.455 0.455 0.445

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Accuracy Across Categories
Class GP1 GT1 GT2 GT3 HW KL LRPPI1 LRPPI2 LRPPI3 LRPPI4 ME1 ME2 ME3
1 0.517 0.542 0.633 0.408 0.175 0.250 0.467 0.492 0.533 0.517 0.450 0.450 0.450
2 0.517 0.550 0.350 0.492 0.100 0.367 0.508 0.467 0.475 0.450 0.358 0.350 0.350
3 0.833 0.775 0.683 0.758 0.817 0.842 0.775 0.792 0.750 0.783 0.550 0.550 0.550
4 0.500 0.417 0.467 0.492 0.100 0.250 0.425 0.417 0.408 0.400 0.492 0.483 0.475
5 0.817 0.467 0.492 0.798 0.325 0.783 0.625 0.608 0.558 0.625 0.667 0.667 0.658

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MIREX 2008 Audio Artist Classification Evaluation Logs and Confusion Matrices

MIREX 2008 Audio Mood Classification Run Times

Participant Runtime (hh:mm) / Fold
GP1 Feat Ex: 01:01 Train/Classify: 00:02
GT1 Feat Ex/Train/Classify: 00:03
GT2 Feat Ex/Train/Classify: 00:07
GT3 Feat Ex: 00:01 Train/Classify: 00:00 (1 sec)
HW Feat Ex/Train/Classify: 09:33
KL Feat Ex/Train/Classify: 00:09
LRPPI1 Feat Ex: 02:48 Train/Classify: 00:00 (11 sec)
LRPPI2 Feat Ex: 02:48 Train/Classify: 00:00 (29 sec)
LRPPI3 Feat Ex: 02:48 Train/Classify: 00:00 (30 sec)
LRPPI4 Feat Ex: 02:48 Train/Classify: 00:00 (46 sec)
ME1 Feat Ex: 0:20 Train/Classify: 00:00 (2 sec)
ME2 Feat Ex: 0:20 Train/Classify: 00:00 (2 sec)
ME3 Feat Ex: 0:20 Train/Classify: 00:00 (2 sec)

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CSV Files Without Rounding

audiomood_results_fold.csv
audiomood_results_class.csv

Results By Algorithm

(.tar.gz)
GP1 = G. Peeters
GT1 = G. Tzanetakis
GT2 = G. Tzanetakis
GT3 = G. Tzanetakis
HW = G. H. Wang
KL = K. Lee
LRPPI1 = T. Lidy, A. Rauber, A. Pertusa, P. Peonce de León, J. M. Iñesta 1
LRPPI2 = T. Lidy, A. Rauber, A. Pertusa, P. Peonce de León, J. M. Iñesta 2
LRPPI3 = T. Lidy, A. Rauber, A. Pertusa, P. Peonce de León, J. M. Iñesta 3
LRPPI4 = T. Lidy, A. Rauber, A. Pertusa, P. Peonce de León, J. M. Iñesta 4
ME1 = I. M. Mandel, D. P. W. Ellis 1
ME2 = I. M. Mandel, D. P. W. Ellis 2
ME3 = I. M. Mandel, D. P. W. Ellis 3

Friedman's Test for Significant Differences

Classes vs. Systems

The Friedman test was run in MATLAB against the average accuracy for each class.

Friedman's Anova Table
Source SS df MS Chi-sq Prob>Chi-sq
Columns 157.2 10 15.72 14.34 0.1579
Error 390.8 40 9.77
Total 548 54

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Tukey-Kramer HSD Multi-Comparison

The Tukey-Kramer HSD multi-comparison data below was generated using the following MATLAB instruction. Command: [c, m, h, gnames] = multicompare(stats, 'ctype', 'tukey-kramer', 'estimate', 'friedman', 'alpha', 0.05);

TeamID TeamID Lowerbound Mean Upperbound Significance
GP1 GT1 -3.6392 3.1000 9.8392 FALSE
GP1 GT2 -3.3392 3.4000 10.1392 FALSE
GP1 GT3 -2.4392 4.3000 11.0392 FALSE
GP1 HW -2.8392 3.9000 10.6392 FALSE
GP1 KL -3.5392 3.2000 9.9392 FALSE
GP1 LRPPI1 -2.1392 4.6000 11.3392 FALSE
GP1 LRPPI2 -1.9392 4.8000 11.5392 FALSE
GP1 LRPPI3 -1.8392 4.9000 11.6392 FALSE
GP1 LRPPI4 -2.3392 4.4000 11.1392 FALSE
GP1 ME1 0.6608 7.4000 14.1392 TRUE
GT1 GT2 -6.4392 0.3000 7.0392 FALSE
GT1 GT3 -5.5392 1.2000 7.9392 FALSE
GT1 HW -5.9392 0.8000 7.5392 FALSE
GT1 KL -6.6392 0.1000 6.8392 FALSE
GT1 LRPPI1 -5.2392 1.5000 8.2392 FALSE
GT1 LRPPI2 -5.0392 1.7000 8.4392 FALSE
GT1 LRPPI3 -4.9392 1.8000 8.5392 FALSE
GT1 LRPPI4 -5.4392 1.3000 8.0392 FALSE
GT1 ME1 -2.4392 4.3000 11.0392 FALSE
GT2 GT3 -5.8392 0.9000 7.6392 FALSE
GT2 HW -6.2392 0.5000 7.2392 FALSE
GT2 KL -6.9392 -0.2000 6.5392 FALSE
GT2 LRPPI1 -5.5392 1.2000 7.9392 FALSE
GT2 LRPPI2 -5.3392 1.4000 8.1392 FALSE
GT2 LRPPI3 -5.2392 1.5000 8.2392 FALSE
GT2 LRPPI4 -5.7392 1.0000 7.7392 FALSE
GT2 ME1 -2.7392 4.0000 10.7392 FALSE
GT3 HW -7.1392 -0.4000 6.3392 FALSE
GT3 KL -7.8392 -1.1000 5.6392 FALSE
GT3 LRPPI1 -6.4392 0.3000 7.0392 FALSE
GT3 LRPPI2 -6.2392 0.5000 7.2392 FALSE
GT3 LRPPI3 -6.1392 0.6000 7.3392 FALSE
GT3 LRPPI4 -6.6392 0.1000 6.8392 FALSE
GT3 ME1 -3.6392 3.1000 9.8392 FALSE
HW KL -7.4392 -0.7000 6.0392 FALSE
HW LRPPI1 -6.0392 0.7000 7.4392 FALSE
HW LRPPI2 -5.8392 0.9000 7.6392 FALSE
HW LRPPI3 -5.7392 1.0000 7.7392 FALSE
HW LRPPI4 -6.2392 0.5000 7.2392 FALSE
HW ME1 -3.2392 3.5000 10.2392 FALSE
KL LRPPI1 -5.3392 1.4000 8.1392 FALSE
KL LRPPI2 -5.1392 1.6000 8.3392 FALSE
KL LRPPI3 -5.0392 1.7000 8.4392 FALSE
KL LRPPI4 -5.5392 1.2000 7.9392 FALSE
KL ME1 -2.5392 4.2000 10.9392 FALSE
LRPPI1 LRPPI2 -6.5392 0.2000 6.9392 FALSE
LRPPI1 LRPPI3 -6.4392 0.3000 7.0392 FALSE
LRPPI1 LRPPI4 -6.9392 -0.2000 6.5392 FALSE
LRPPI1 ME1 -3.9392 2.8000 9.5392 FALSE
LRPPI2 LRPPI3 -6.6392 0.1000 6.8392 FALSE
LRPPI2 LRPPI4 -7.1392 -0.4000 6.3392 FALSE
LRPPI2 ME1 -4.1392 2.6000 9.3392 FALSE
LRPPI3 LRPPI4 -7.2392 -0.5000 6.2392 FALSE
LRPPI3 ME1 -4.2392 2.5000 9.2392 FALSE
LRPPI4 ME1 -3.7392 3.0000 9.7392 FALSE

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2008 mood.perclassaccuracy.friedman.tukeykramerhsd.png

Folds vs. Systems

The Friedman test was run in MATLAB against the accuracy for each fold.

Friedman's Anova Table
Source SS df MS Chi-sq Prob>Chi-sq
Columns 208.167 10 20.8167 18.95 0.0409
Error 121.333 20 6.0667
Total 329.5 32

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Tukey-Kramer HSD Multi-Comparison

The Tukey-Kramer HSD multi-comparison data below was generated using the following MATLAB instruction. Command: [c, m, h, gnames] = multicompare(stats, 'ctype', 'tukey-kramer', 'estimate', 'friedman', 'alpha', 0.05);

TeamID TeamID Lowerbound Mean Upperbound Significance
GP1 GT1 -6.3762 2.3333 11.0429 FALSE
GP1 GT2 -4.3762 4.3333 13.0429 FALSE
GP1 GT3 -4.2095 4.5000 13.2095 FALSE
GP1 HW -4.0429 4.6667 13.3762 FALSE
GP1 KL -4.7095 4.0000 12.7095 FALSE
GP1 LRPPI1 -3.3762 5.3333 14.0429 FALSE
GP1 LRPPI2 -3.3762 5.3333 14.0429 FALSE
GP1 LRPPI3 -1.2095 7.5000 16.2095 FALSE
GP1 LRPPI4 -1.7095 7.0000 15.7095 FALSE
GP1 ME1 1.2905 10.0000 18.7095 TRUE
GT1 GT2 -6.7095 2.0000 10.7095 FALSE
GT1 GT3 -6.5429 2.1667 10.8762 FALSE
GT1 HW -6.3762 2.3333 11.0429 FALSE
GT1 KL -7.0429 1.6667 10.3762 FALSE
GT1 LRPPI1 -5.7095 3.0000 11.7095 FALSE
GT1 LRPPI2 -5.7095 3.0000 11.7095 FALSE
GT1 LRPPI3 -3.5429 5.1667 13.8762 FALSE
GT1 LRPPI4 -4.0429 4.6667 13.3762 FALSE
GT1 ME1 -1.0429 7.6667 16.3762 FALSE
GT2 GT3 -8.5429 0.1667 8.8762 FALSE
GT2 HW -8.3762 0.3333 9.0429 FALSE
GT2 KL -9.0429 -0.3333 8.3762 FALSE
GT2 LRPPI1 -7.7095 1.0000 9.7095 FALSE
GT2 LRPPI2 -7.7095 1.0000 9.7095 FALSE
GT2 LRPPI3 -5.5429 3.1667 11.8762 FALSE
GT2 LRPPI4 -6.0429 2.6667 11.3762 FALSE
GT2 ME1 -3.0429 5.6667 14.3762 FALSE
GT3 HW -8.5429 0.1667 8.8762 FALSE
GT3 KL -9.2095 -0.5000 8.2095 FALSE
GT3 LRPPI1 -7.8762 0.8333 9.5429 FALSE
GT3 LRPPI2 -7.8762 0.8333 9.5429 FALSE
GT3 LRPPI3 -5.7095 3.0000 11.7095 FALSE
GT3 LRPPI4 -6.2095 2.5000 11.2095 FALSE
GT3 ME1 -3.2095 5.5000 14.2095 FALSE
HW KL -9.3762 -0.6667 8.0429 FALSE
HW LRPPI1 -8.0429 0.6667 9.3762 FALSE
HW LRPPI2 -8.0429 0.6667 9.3762 FALSE
HW LRPPI3 -5.8762 2.8333 11.5429 FALSE
HW LRPPI4 -6.3762 2.3333 11.0429 FALSE
HW ME1 -3.3762 5.3333 14.0429 FALSE
KL LRPPI1 -7.3762 1.3333 10.0429 FALSE
KL LRPPI2 -7.3762 1.3333 10.0429 FALSE
KL LRPPI3 -5.2095 3.5000 12.2095 FALSE
KL LRPPI4 -5.7095 3.0000 11.7095 FALSE
KL ME1 -2.7095 6.0000 14.7095 FALSE
LRPPI1 LRPPI2 -8.7095 0.0000 8.7095 FALSE
LRPPI1 LRPPI3 -6.5429 2.1667 10.8762 FALSE
LRPPI1 LRPPI4 -7.0429 1.6667 10.3762 FALSE
LRPPI1 ME1 -4.0429 4.6667 13.3762 FALSE
LRPPI2 LRPPI3 -6.5429 2.1667 10.8762 FALSE
LRPPI2 LRPPI4 -7.0429 1.6667 10.3762 FALSE
LRPPI2 ME1 -4.0429 4.6667 13.3762 FALSE
LRPPI3 LRPPI4 -9.2095 -0.5000 8.2095 FALSE
LRPPI3 ME1 -6.2095 2.5000 11.2095 FALSE
LRPPI4 ME1 -5.7095 3.0000 11.7095 FALSE

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2008 mood.perfoldaccuracy.friedman.tukeykramerhsd.png

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