Difference between revisions of "2009:Audio Music Mood Classification Results"
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Revision as of 20:41, 15 October 2009
Contents
- 1 Introduction
- 2 Overall Summary Results
- 3 Overall Summary Results
Introduction
These are the results for the 2009 running of the Audio Music Mood Classification task. For background information about this task set please refer to the Audio Music Mood Classification page.
General Legend
Team ID
ANO= Anonymous
BP1= [Juan José Burred, Geoffroy Peeters (file)]
BP2 = [Juan José Burred, Geoffroy Peeters (tw)]
CL1 = [Chuan Cao, Ming Li]
CL2 = [Chuan Cao, Ming Li]
FCY1 = [Tao Feng, XiaoOu Chen, DeShun Yang]
FCY2 = [Tao Feng, XiaoOu Chen, DeShun Yang]
GP = [Geoffroy Peeters]
GT1 = [George Tzanetakis (mono)]
GT2 = [George Tzanetakis (stereo)]
GLR1 = [A. Grecu, T. Lidy, A. Rauber (full)]
GLR2 = [A. Grecu, T. Lidy, A. Rauber (template)]
HNOS1 = [Takashi Hasegawa, Takuya Nishimoto, Nobutaka Ono, Shigeki Sagayama (tcca)]
HNOS2 = [Takashi Hasegawa, Takuya Nishimoto, Nobutaka Ono, Shigeki Sagayama (tcck)]
HNOS3 = [Takashi Hasegawa, Takuya Nishimoto, Nobutaka Ono, Shigeki Sagayama (tccl)]
HNOS4 = [Takashi Hasegawa, Takuya Nishimoto, Nobutaka Ono, Shigeki Sagayama (tcpk)]
HW1 = [Huaxin Wang]
HW2 = [Huaxin Wang]
VA1 = [T. Lidy, A. Grecu, A. Rauber, A. Pertusa, P. J. Ponce de Léon, J. M. Iñesta (WMV)]
VA2 = [T. Lidy, A. Grecu, A. Rauber, A. Pertusa, P. J. Ponce de Léon, J. M. Iñesta (BWWV)]
LZG = [Yi Liu, Tao Zheng, Yue Gao (RUC_1)]
RK1 = [Preeti Rao, Sujeet Kini]
RK2 = [Preeti Rao, Sujeet Kini]
RCJ1 = [Jia-Min Ren, Zhi-Sheng Chen, Jyh-Shing Roger Jang]
RCJ2 = [Jia-Min Ren, Zhi-Sheng Chen, Jyh-Shing Roger Jang]
RCJ3 = [Jia-Min Ren, Zhi-Sheng Chen, Jyh-Shing Roger Jang]
RCJ4 = [Jia-Min Ren, Zhi-Sheng Chen, Jyh-Shing Roger Jang]
SS = [Klaus Seyerlehner, Markus Schedl]
TAOS= [Emiru Tsunoo, Taichi Akase, Nobutaka Ono, Shigeki Sagayama]
TTOS = [Emiru Tsunoo, George Tzanetakis, Nobutaka Ono, Shigeki Sagayama]
MTG1 = [N. Wack, E. Guaus, C. Laurier, O. Meyers, R. Marxer, D. Bogdanov, J. Serrà, P. Herrera (false, rca)]
MTG2 = [N. Wack, E. Guaus, C. Laurier, O. Meyers, R. Marxer, D. Bogdanov, J. Serrà, P. Herrera (true, rca)]
MTG3 = [N. Wack, E. Guaus, C. Laurier, O. Meyers, R. Marxer, D. Bogdanov, J. Serrà, P. Herrera (false, simca)]
MTG4 = [N. Wack, E. Guaus, C. Laurier, O. Meyers, R. Marxer, D. Bogdanov, J. Serrà, P. Herrera (true, simca)]
MTG5 = [N. Wack, E. Guaus, C. Laurier, O. Meyers, R. Marxer, D. Bogdanov, J. Serrà, P. Herrera (false, svm)]
MTG6 = [N. Wack, E. Guaus, C. Laurier, O. Meyers, R. Marxer, D. Bogdanov, J. Serrà, P. Herrera (true, svm)]
XLZZG = [Jieping Xu, Yi Liu, Tao Zheng, Chao Zhen, Yue Gao (RUC_1)]
XZZ = [JiePing Xu, Chao Zhen, Tao Zheng (RUC_2)]
Overall Summary Results
Overall Summary Results
MIREX 2009 Audio Mood Classification Summary Results - Raw Classification Accuracy Averaged Over Three Train/Test Folds
file /nema-raid/www/mirex/results/audiomood/summary_audiomood.csv not found
Accuracy Across Folds
file /nema-raid/www/mirex/results/audiomood/audiomood_Accuracy.csv not found
Accuracy Across Categories
file /nema-raid/www/mirex/results/audiomood/audiomood_Accuracy_Per_Class.csv not found
MIREX 2008 Audio Artist Classification Evaluation Logs and Confusion Matrices
MIREX 2008 Audio Mood Classification Run Times
file /nema-raid/www/mirex/results/mood.runtime.csv not found
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 Leon, J. M. I├▒esta 1
LRPPI2 = T. Lidy, A. Rauber, A. Pertusa, P. Peonce de Leon, J. M. I├▒esta 2
LRPPI3 = T. Lidy, A. Rauber, A. Pertusa, P. Peonce de Leon, J. M. I├▒esta 3
LRPPI4 = T. Lidy, A. Rauber, A. Pertusa, P. Peonce de Leon, J. M. I├▒esta 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
file /nema-raid/www/mirex/results/mood/perClassAccuracy.friedman.csv not found
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);
file /nema-raid/www/mirex/results/mood/perClassAccuracy.friedman.detail.csv not found
File: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
file /nema-raid/www/mirex/results/mood/perFoldAccuracy.friedman.csv not found
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);
file /nema-raid/www/mirex/results/mood/perFoldAccuracy.friedman.detail.csv not found