Difference between revisions of "2008:Audio Genre Classification Results"
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Command: [c, m, h, gnames] = multicompare(stats, 'ctype', 'tukey-kramer', 'estimate', 'friedman', 'alpha', 0.05); | Command: [c, m, h, gnames] = multicompare(stats, 'ctype', 'tukey-kramer', 'estimate', 'friedman', 'alpha', 0.05); | ||
| − | <csv> | + | <csv>genremixed/perClassAccuracy.friedman.detail.csv</csv> |
| − | [[Image: | + | [[Image:Genremixed.perClassAccuracy.friedman.tukeyKramerHSD.png]] |
====Task 1 (Mixed) Folds vs. Systems==== | ====Task 1 (Mixed) Folds vs. Systems==== | ||
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| + | ====Task 2 (Latin) Classes vs. Systems==== | ||
| + | The Friedman test was run in MATLAB against the average accuracy for each class. | ||
| + | |||
| + | =====Friedman's Anova Table===== | ||
| + | |||
| + | <csv>genrelatin/perClassAccuracy.friedman.csv</csv> | ||
| + | |||
| + | =====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); | ||
| + | |||
| + | <csv>genrelatin/perClassAccuracy.friedman.detail.csv</csv> | ||
| + | |||
| + | [[Image:Genrelatin.perClassAccuracy.friedman.tukeyKramerHSD.png]] | ||
| + | |||
| + | ====Task 2 (Latin) Folds vs. Systems==== | ||
| + | The Friedman test was run in MATLAB against the accuracy for each fold. | ||
| + | |||
| + | =====Friedman's Anova Table===== | ||
| + | |||
| + | <csv>genrelatin/perFoldAccuracy.friedman.csv</csv> | ||
| + | |||
| + | =====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); | ||
| + | |||
| + | <csv>genrelatin/perFoldAccuracy.friedman.detail.csv</csv> | ||
| + | |||
| + | [[Image:Genrelatin.perFoldAccuracy.friedman.tukeyKramerHSD.png]] | ||
[[Category: Results]] | [[Category: Results]] | ||
Revision as of 11:15, 11 September 2008
Contents
- 1 Introduction
- 2 Overall Summary Results
- 2.1 Task 1 (MIXED) Results
- 2.1.1 MIREX 2008 Audio Genre Classification Summary Results - Raw Classification Accuracy Averaged Over Three Train/Test Folds
- 2.1.2 MIREX 2008 Audio Genre Classification Evaluation Logs and Confusion Matrices
- 2.1.3 MIREX 2008 Audio Genre Classification Run Times
- 2.1.4 CSV Files Without Rounding
- 2.1.5 Results By Algorithm
- 2.2 Task 2 (LATIN) Results
- 2.2.1 MIREX 2008 Audio Genre Classification Summary Results - Raw Classification Accuracy Averaged Over Three Train/Test Folds
- 2.2.2 MIREX 2008 Audio Genre Classification Evaluation Logs and Confusion Matrices
- 2.2.3 MIREX 2008 Audio Genre Classification Run Times
- 2.2.4 CSV Files Without Rounding
- 2.2.5 Results By Algorithm
- 2.3 Friedman's Test for Significant Differences
- 2.1 Task 1 (MIXED) Results
Introduction
These are the results for the 2008 running of the Audio Genre Classification task. For background information about this task set please refer to the Audio Genre Classification page.
General Legend
Team ID
CL1 = C. Cao, M. Li 1
CL2 = C. Cao, M. Li 2
GP1 = G. Peeters
GT1 (mono) = G. Tzanetakis
GT2 (stereo) = G. Tzanetakis
GT3 (multicore) = G. Tzanetakis
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
Overall Summary Results
Task 1 (MIXED) Results
MIREX 2008 Audio Genre Classification Summary Results - Raw Classification Accuracy Averaged Over Three Train/Test Folds
file /nema-raid/www/mirex/results/genremixed/audiogenre.avg.results.csv not found
Accuracy Across Folds
file /nema-raid/www/mirex/results/genremixed/audiogenre.results.fold.csv not found
Accuracy Across Categories
file /nema-raid/www/mirex/results/genremixed/audiogenre.results.class.csv not found
MIREX 2008 Audio Genre Classification Evaluation Logs and Confusion Matrices
MIREX 2008 Audio Genre Classification Run Times
file /nema-raid/www/mirex/results/genre.runtime.csv not found
CSV Files Without Rounding
audiogenre_results_fold.csv
audiogenre_results_class.csv
Results By Algorithm
(.tar.gz)
CL1 = C. Cao, M. Li 1
CL2 = C. Cao, M. Li 2
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
GP = G. Peeters
GT1 = G. Tzanetakis
GT2 = G. Tzanetakis
GT3 = G. Tzanetakis
Task 2 (LATIN) Results
MIREX 2008 Audio Genre Classification Summary Results - Raw Classification Accuracy Averaged Over Three Train/Test Folds
file /nema-raid/www/mirex/results/genrelatin/audiolatin.avg.results.csv not found
Accuracy Across Folds
file /nema-raid/www/mirex/results/genrelatin/audiolatin.results.fold.csv not found
Accuracy Across Categories
file /nema-raid/www/mirex/results/genrelatin/audiolatin.results.class.csv not found
MIREX 2008 Audio Genre Classification Evaluation Logs and Confusion Matrices
MIREX 2008 Audio Genre Classification Run Times
file /nema-raid/www/mirex/results/latin.runtime.csv not found
CSV Files Without Rounding
audiolatin_results_fold.csv
audiolatin_results_class.csv
Results By Algorithm
(.tar.gz)
CL1 = C. Cao, M. Li 1
CL2 = C. Cao, M. Li 2
GP1 = G. Peeters
GT1 = G. Tzanetakis
GT2 = G. Tzanetakis
GT3 = G. Tzanetakis
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
Task 1 (Mixed) 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/genremixed/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/genremixed/perClassAccuracy.friedman.detail.csv not found
File:Genremixed.perClassAccuracy.friedman.tukeyKramerHSD.png
Task 1 (Mixed) 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/genremixed/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/genremixed/perFoldAccuracy.friedman.detail.csv not found
File:Genremixed.perFoldAccuracy.friedman.tukeyKramerHSD.png
Task 2 (Latin) 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/genrelatin/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/genrelatin/perClassAccuracy.friedman.detail.csv not found
File:Genrelatin.perClassAccuracy.friedman.tukeyKramerHSD.png
Task 2 (Latin) 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/genrelatin/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/genrelatin/perFoldAccuracy.friedman.detail.csv not found
