Difference between revisions of "2008:Audio Classical Composer Identification Results"

Latest revision as of 16:07, 23 July 2010

Introduction

These are the results for the 2008 running of the Audio Classical Composer Identification task. For background information about this task set please refer to the 2007:Audio Classical Composer Identification page.

The data set consisted of 2772 30 second audio clips. The composers represented were:

Bach
Beethoven
Brahms
Chopin
Dvorak
Handel
Haydn
Mendelssohnn
Mozart
Schubert
Vivaldi

The goal was to correctly identify the composer who wrote each of the pieces represented.

General Legend

Team ID

GP1 = G. Peeters
GT1 = G. Tzanetakis
GT2 = G. Tzanetakis
GT3 = G. Tzanetakis
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 Classical Composer Classification Summary Results - Raw Classification Accuracy Averaged Over Three Train/Test Folds

Participant	Average Classifcation Accuracy
GP1	48.99%
GT1	39.47%
GT2	45.82%
GT3	43.81%
LRPPI1	34.13%
LRPPI2	39.43%
LRPPI3	37.48%
LRPPI4	39.54%
ME1	53.25%
ME2	53.10%
ME3	52.89%

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Accuracy Across Folds

Classification fold	GP1	GT1	GT2	GT3	LRPPI1	LRPPI2	LRPPI3	LRPPI4	ME1	ME2	ME3
0	0.501	0.363	0.452	0.457	0.165	0.386	0.379	0.389	0.545	0.538	0.532
1	0.483	0.415	0.464	0.424	0.431	0.406	0.369	0.395	0.523	0.525	0.527
2	0.486	0.407	0.458	0.433	0.429	0.391	0.377	0.403	0.529	0.530	0.527

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Accuracy Across Categories

Class	GP1	GT1	GT2	GT3	LRPPI1	LRPPI2	LRPPI3	LRPPI4	ME1	ME2	ME3
bach	0.667	0.516	0.651	0.571	0.563	0.575	0.500	0.583	0.734	0.738	0.738
beethoven	0.321	0.409	0.548	0.425	0.198	0.310	0.266	0.282	0.393	0.385	0.393
brahms	0.290	0.159	0.198	0.230	0.198	0.321	0.310	0.310	0.429	0.429	0.433
chopin	0.913	0.885	0.897	0.810	0.595	0.663	0.627	0.659	0.770	0.774	0.766
dvorak	0.417	0.333	0.488	0.484	0.393	0.369	0.393	0.361	0.456	0.444	0.448
handel	0.492	0.310	0.302	0.321	0.397	0.425	0.377	0.405	0.548	0.548	0.544
haydn	0.655	0.488	0.651	0.556	0.369	0.413	0.397	0.460	0.603	0.607	0.603
mendelssohnn	0.337	0.472	0.492	0.401	0.286	0.401	0.306	0.341	0.488	0.472	0.468
mozart	0.266	0.087	0.079	0.242	0.234	0.246	0.274	0.214	0.353	0.353	0.345
schubert	0.302	0.194	0.230	0.210	0.175	0.218	0.262	0.278	0.417	0.425	0.425
vivaldi	0.730	0.488	0.504	0.567	0.345	0.397	0.413	0.456	0.667	0.667	0.655

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MIREX 2008 Audio Classical Composer Classification Evaluation Logs and Confusion Matrices

MIREX 2008 Audio Classical Composer Classification Run Times

Participant	Runtime (hh:mm) / Fold
GP1	Feat Ex: 04:40 Train/Classify: 00:47
GT1	Feat Ex/Train/Classify: 00:16
GT2	Feat Ex/Train/Classify: 00:34
GT3	Feat Ex: 00:05 Train/Classify: 00:00 (7 sec)
LRPPI1	Feat Ex: 08:00 Train/Classify: 00:02
LRPPI2	Feat Ex: 08:00 Train/Classify: 00:09
LRPPI3	Feat Ex: 08:00 Train/Classify: 00:09
LRPPI4	Feat Ex: 08:00 Train/Classify: 00:14
ME1	Feat Ex: 1:17 Train/Classify: 00:00 (21 sec)
ME2	Feat Ex: 1:17 Train/Classify: 00:00 (21 sec)
ME3	Feat Ex: 1:17 Train/Classify: 00:00 (21 sec)

download these results as csv

CSV Files Without Rounding

audiocomposer_results_fold.csv
audiocomposer_results_class.csv

Results By Algorithm

(.tar.gz)
GP1 = G. Peeters
GT1 = G. Tzanetakis
GT2 = G. Tzanetakis
GT3 = G. Tzanetakis
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	581.36	10	58.1364	53.09	7.16E-08
Error	623.14	100	6.2314
Total	1204.5	120

download these results as 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);

TeamID	TeamID	Lowerbound	Mean	Upperbound	Significance
GP1	GT1	-4.6324	-0.0909	4.4506	FALSE
GP1	GT2	-4.1779	0.3636	4.9051	FALSE
GP1	GT3	-3.2233	1.3182	5.8597	FALSE
GP1	LRPPI1	-2.5415	2.0000	6.5415	FALSE
GP1	LRPPI2	-1.6324	2.9091	7.4506	FALSE
GP1	LRPPI3	-0.1324	4.4091	8.9506	FALSE
GP1	LRPPI4	0.0949	4.6364	9.1779	TRUE
GP1	ME1	-0.3597	4.1818	8.7233	FALSE
GP1	ME2	0.6403	5.1818	9.7233	TRUE
GP1	ME3	2.0494	6.5909	11.1324	TRUE
GT1	GT2	-4.0870	0.4545	4.9961	FALSE
GT1	GT3	-3.1324	1.4091	5.9506	FALSE
GT1	LRPPI1	-2.4506	2.0909	6.6324	FALSE
GT1	LRPPI2	-1.5415	3.0000	7.5415	FALSE
GT1	LRPPI3	-0.0415	4.5000	9.0415	FALSE
GT1	LRPPI4	0.1858	4.7273	9.2688	TRUE
GT1	ME1	-0.2688	4.2727	8.8142	FALSE
GT1	ME2	0.7312	5.2727	9.8142	TRUE
GT1	ME3	2.1403	6.6818	11.2233	TRUE
GT2	GT3	-3.5870	0.9545	5.4961	FALSE
GT2	LRPPI1	-2.9051	1.6364	6.1779	FALSE
GT2	LRPPI2	-1.9961	2.5455	7.0870	FALSE
GT2	LRPPI3	-0.4961	4.0455	8.5870	FALSE
GT2	LRPPI4	-0.2688	4.2727	8.8142	FALSE
GT2	ME1	-0.7233	3.8182	8.3597	FALSE
GT2	ME2	0.2767	4.8182	9.3597	TRUE
GT2	ME3	1.6858	6.2273	10.7688	TRUE
GT3	LRPPI1	-3.8597	0.6818	5.2233	FALSE
GT3	LRPPI2	-2.9506	1.5909	6.1324	FALSE
GT3	LRPPI3	-1.4506	3.0909	7.6324	FALSE
GT3	LRPPI4	-1.2233	3.3182	7.8597	FALSE
GT3	ME1	-1.6779	2.8636	7.4051	FALSE
GT3	ME2	-0.6779	3.8636	8.4051	FALSE
GT3	ME3	0.7312	5.2727	9.8142	TRUE
LRPPI1	LRPPI2	-3.6324	0.9091	5.4506	FALSE
LRPPI1	LRPPI3	-2.1324	2.4091	6.9506	FALSE
LRPPI1	LRPPI4	-1.9051	2.6364	7.1779	FALSE
LRPPI1	ME1	-2.3597	2.1818	6.7233	FALSE
LRPPI1	ME2	-1.3597	3.1818	7.7233	FALSE
LRPPI1	ME3	0.0494	4.5909	9.1324	TRUE
LRPPI2	LRPPI3	-3.0415	1.5000	6.0415	FALSE
LRPPI2	LRPPI4	-2.8142	1.7273	6.2688	FALSE
LRPPI2	ME1	-3.2688	1.2727	5.8142	FALSE
LRPPI2	ME2	-2.2688	2.2727	6.8142	FALSE
LRPPI2	ME3	-0.8597	3.6818	8.2233	FALSE
LRPPI3	LRPPI4	-4.3142	0.2273	4.7688	FALSE
LRPPI3	ME1	-4.7688	-0.2273	4.3142	FALSE
LRPPI3	ME2	-3.7688	0.7727	5.3142	FALSE
LRPPI3	ME3	-2.3597	2.1818	6.7233	FALSE
LRPPI4	ME1	-4.9961	-0.4545	4.0870	FALSE
LRPPI4	ME2	-3.9961	0.5455	5.0870	FALSE
LRPPI4	ME3	-2.5870	1.9545	6.4961	FALSE
ME1	ME2	-3.5415	1.0000	5.5415	FALSE
ME1	ME3	-2.1324	2.4091	6.9506	FALSE
ME2	ME3	-3.1324	1.4091	5.9506	FALSE

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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	296	10	29.6	26.91	0.0027
Error	34	20	1.7
Total	330	32

download these results as 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);

TeamID	TeamID	Lowerbound	Mean	Upperbound	Significance
GP1	GT1	-9.0495	-0.3333	8.3828	FALSE
GP1	GT2	-8.3828	0.3333	9.0495	FALSE
GP1	GT3	-6.7162	2.0000	10.7162	FALSE
GP1	LRPPI1	-5.3828	3.3333	12.0495	FALSE
GP1	LRPPI2	-4.7162	4.0000	12.7162	FALSE
GP1	LRPPI3	-2.0495	6.6667	15.3828	FALSE
GP1	LRPPI4	-2.0495	6.6667	15.3828	FALSE
GP1	ME1	-1.7162	7.0000	15.7162	FALSE
GP1	ME2	-0.3828	8.3333	17.0495	FALSE
GP1	ME3	-2.7162	6.0000	14.7162	FALSE
GT1	GT2	-8.0495	0.6667	9.3828	FALSE
GT1	GT3	-6.3828	2.3333	11.0495	FALSE
GT1	LRPPI1	-5.0495	3.6667	12.3828	FALSE
GT1	LRPPI2	-4.3828	4.3333	13.0495	FALSE
GT1	LRPPI3	-1.7162	7.0000	15.7162	FALSE
GT1	LRPPI4	-1.7162	7.0000	15.7162	FALSE
GT1	ME1	-1.3828	7.3333	16.0495	FALSE
GT1	ME2	-0.0495	8.6667	17.3828	FALSE
GT1	ME3	-2.3828	6.3333	15.0495	FALSE
GT2	GT3	-7.0495	1.6667	10.3828	FALSE
GT2	LRPPI1	-5.7162	3.0000	11.7162	FALSE
GT2	LRPPI2	-5.0495	3.6667	12.3828	FALSE
GT2	LRPPI3	-2.3828	6.3333	15.0495	FALSE
GT2	LRPPI4	-2.3828	6.3333	15.0495	FALSE
GT2	ME1	-2.0495	6.6667	15.3828	FALSE
GT2	ME2	-0.7162	8.0000	16.7162	FALSE
GT2	ME3	-3.0495	5.6667	14.3828	FALSE
GT3	LRPPI1	-7.3828	1.3333	10.0495	FALSE
GT3	LRPPI2	-6.7162	2.0000	10.7162	FALSE
GT3	LRPPI3	-4.0495	4.6667	13.3828	FALSE
GT3	LRPPI4	-4.0495	4.6667	13.3828	FALSE
GT3	ME1	-3.7162	5.0000	13.7162	FALSE
GT3	ME2	-2.3828	6.3333	15.0495	FALSE
GT3	ME3	-4.7162	4.0000	12.7162	FALSE
LRPPI1	LRPPI2	-8.0495	0.6667	9.3828	FALSE
LRPPI1	LRPPI3	-5.3828	3.3333	12.0495	FALSE
LRPPI1	LRPPI4	-5.3828	3.3333	12.0495	FALSE
LRPPI1	ME1	-5.0495	3.6667	12.3828	FALSE
LRPPI1	ME2	-3.7162	5.0000	13.7162	FALSE
LRPPI1	ME3	-6.0495	2.6667	11.3828	FALSE
LRPPI2	LRPPI3	-6.0495	2.6667	11.3828	FALSE
LRPPI2	LRPPI4	-6.0495	2.6667	11.3828	FALSE
LRPPI2	ME1	-5.7162	3.0000	11.7162	FALSE
LRPPI2	ME2	-4.3828	4.3333	13.0495	FALSE
LRPPI2	ME3	-6.7162	2.0000	10.7162	FALSE
LRPPI3	LRPPI4	-8.7162	0.0000	8.7162	FALSE
LRPPI3	ME1	-8.3828	0.3333	9.0495	FALSE
LRPPI3	ME2	-7.0495	1.6667	10.3828	FALSE
LRPPI3	ME3	-9.3828	-0.6667	8.0495	FALSE
LRPPI4	ME1	-8.3828	0.3333	9.0495	FALSE
LRPPI4	ME2	-7.0495	1.6667	10.3828	FALSE
LRPPI4	ME3	-9.3828	-0.6667	8.0495	FALSE
ME1	ME2	-7.3828	1.3333	10.0495	FALSE
ME1	ME3	-9.7162	-1.0000	7.7162	FALSE
ME2	ME3	-11.0495	-2.3333	6.3828	FALSE

download these results as csv

@@ Line 1: / Line 1: @@
 ==Introduction==
-These are the results for the 2008 running of the Audio Classical Composer Identification task. For background information about this task set please refer to the [[Audio Classical Composer Identification]] page.
+These are the results for the 2008 running of the Audio Classical Composer Identification task. For background information about this task set please refer to the [[2007:Audio Classical Composer Identification]] page.
+The data set consisted of 2772 30 second audio clips. The composers represented were:
+#Bach
+#Beethoven
+#Brahms
+#Chopin
+#Dvorak
+#Handel
+#Haydn
+#Mendelssohnn
+#Mozart
+#Schubert
+#Vivaldi
+The goal was to correctly identify the composer who wrote each of the pieces represented.
 ===General Legend===
 ====Team ID====
-'''GP1''' = [https://www.music-ir.org/mirex/2008/abs/.pdf G. Peeters]<br />
+'''GP1''' = [https://www.music-ir.org/mirex/abstracts/2008/Peeters_2008_ISMIR_MIREX.pdf G. Peeters]<br />
-'''GT1''' = [https://www.music-ir.org/mirex/2008/abs/.pdf G. Tzanetakis]<br />
+'''GT1''' = [https://www.music-ir.org/mirex/abstracts/2008/mirex2007.pdf G. Tzanetakis]<br />
-'''GT2''' = [https://www.music-ir.org/mirex/2008/abs/.pdf G. Tzanetakis]<br />
+'''GT2''' = [https://www.music-ir.org/mirex/abstracts/2008/mirex2007.pdf G. Tzanetakis]<br />
-'''GT3''' = [https://www.music-ir.org/mirex/2008/abs/.pdf G. Tzanetakis]<br />
+'''GT3''' = [https://www.music-ir.org/mirex/abstracts/2008/mirex2007.pdf G. Tzanetakis]<br />
-'''LRPPI1''' = [https://www.music-ir.org/mirex/2008/abs/.pdf T. Lidy, A. Rauber, A. Pertusa, P. Peonce de Leon, J. M. I├▒esta 1]<br />
+'''LRPPI1''' = [https://www.music-ir.org/mirex/abstracts/2008/abstract_mirex08_class.pdf T. Lidy, A. Rauber, A. Pertusa, P. Peonce de León, J. M. Iñesta 1]<br />
-'''LRPPI2''' = [https://www.music-ir.org/mirex/2008/abs/.pdf T. Lidy, A. Rauber, A. Pertusa, P. Peonce de Leon, J. M. I├▒esta 2]<br />
+'''LRPPI2''' = [https://www.music-ir.org/mirex/abstracts/2008/abstract_mirex08_class.pdf T. Lidy, A. Rauber, A. Pertusa, P. Peonce de León, J. M. Iñesta 2]<br />
-'''LRPPI3''' = [https://www.music-ir.org/mirex/2008/abs/.pdf T. Lidy, A. Rauber, A. Pertusa, P. Peonce de Leon, J. M. I├▒esta 3]<br />
+'''LRPPI3''' = [https://www.music-ir.org/mirex/abstracts/2008/abstract_mirex08_class.pdf T. Lidy, A. Rauber, A. Pertusa, P. Peonce de León, J. M. Iñesta 3]<br />
-'''LRPPI4''' = [https://www.music-ir.org/mirex/2008/abs/.pdf T. Lidy, A. Rauber, A. Pertusa, P. Peonce de Leon, J. M. I├▒esta 4]<br />
+'''LRPPI4''' = [https://www.music-ir.org/mirex/abstracts/2008/abstract_mirex08_class.pdf T. Lidy, A. Rauber, A. Pertusa, P. Peonce de León, J. M. Iñesta 4]<br />
-'''ME1''' = [https://www.music-ir.org/mirex/2008/abs/AI_CC_GC_MC_AS_mandel.pdf M. I. Mandel, D. P. W. Ellis 1]<br />
+'''ME1''' = [https://www.music-ir.org/mirex/abstracts/2008/AA_AG_AT_MM_CC_mandel.pdf M. I. Mandel, D. P. W. Ellis 1]<br />
-'''ME2''' = [https://www.music-ir.org/mirex/2008/abs/AI_CC_GC_MC_AS_mandel.pdf M. I. Mandel, D. P. W. Ellis 2]<br />
+'''ME2''' = [https://www.music-ir.org/mirex/abstracts/2008/AA_AG_AT_MM_CC_mandel.pdf M. I. Mandel, D. P. W. Ellis 2]<br />
-'''ME3''' = [https://www.music-ir.org/mirex/2008/abs/AI_CC_GC_MC_AS_mandel.pdf M. I. Mandel, D. P. W. Ellis 3]<br />
+'''ME3''' = [https://www.music-ir.org/mirex/abstracts/2008/AA_AG_AT_MM_CC_mandel.pdf M. I. Mandel, D. P. W. Ellis 3]<br />
 ==Overall Summary Results==
 ===MIREX 2008 Audio Classical Composer Classification Summary Results - Raw Classification Accuracy Averaged Over Three Train/Test Folds===
-<csv>composer/audiocomposer.avg.results.csv</csv>
+<csv>2008/composer/audiocomposer.avg.results.csv</csv>
 =====Accuracy Across Folds=====
-<csv>composer/audiocomposer.results.fold.csv</csv>
+<csv>2008/composer/audiocomposer.results.fold.csv</csv>
 =====Accuracy Across Categories=====
-<csv>composer/audiocomposer.results.class.csv</csv>
+<csv>2008/composer/audiocomposer.results.class.csv</csv>
 ===MIREX 2008 Audio Classical Composer Classification Evaluation Logs and Confusion Matrices===
-===MIREX 2008 Audio Classical Composer Classification Run Times===
+====MIREX 2008 Audio Classical Composer Classification Run Times====
-<csv>composer.runtime.csv</csv>
+<csv>2008/composer.runtime.csv</csv>
 ====CSV Files Without Rounding====
-[https://www.music-ir.org/mirex/2008/results/composer/audiocomposer_results_fold.csv audiocomposer_results_fold.csv]<br />
+[https://www.music-ir.org/mirex/results/2008/composer/audiocomposer_results_fold.csv audiocomposer_results_fold.csv]<br />
-[https://www.music-ir.org/mirex/2008/results/composer/audiocomposer_results_class.csv audiocomposer_results_class.csv]<br />
+[https://www.music-ir.org/mirex/results/2008/composer/audiocomposer_results_class.csv audiocomposer_results_class.csv]<br />
 ====Results By Algorithm====
 (.tar.gz) <br />
-'''GP1''' = [https://www.music-ir.org/mirex/2008/results/composer/GP1.tar.gz G. Peeters]<br />
+'''GP1''' = [https://www.music-ir.org/mirex/results/2008/composer/GP1.tar.gz G. Peeters]<br />
-'''GT1''' = [https://www.music-ir.org/mirex/2008/results/composer/GT1.tar.gz G. Tzanetakis]<br />
+'''GT1''' = [https://www.music-ir.org/mirex/results/2008/composer/GT1.tar.gz G. Tzanetakis]<br />
-'''GT2''' = [https://www.music-ir.org/mirex/2008/results/composer/GT2.tar.gz G. Tzanetakis]<br />
+'''GT2''' = [https://www.music-ir.org/mirex/results/2008/composer/GT2.tar.gz G. Tzanetakis]<br />
-'''GT3''' = [https://www.music-ir.org/mirex/2008/results/composer/GT3.tar.gz G. Tzanetakis]<br />
+'''GT3''' = [https://www.music-ir.org/mirex/results/2008/composer/GT3.tar.gz G. Tzanetakis]<br />
-'''LRPPI1''' = [https://www.music-ir.org/mirex/2008/results/composer/LRPPI1.tar.gz T. Lidy, A. Rauber, A. Pertusa, P. Peonce de Leon, J. M. I├▒esta 1]<br />
+'''LRPPI1''' = [https://www.music-ir.org/mirex/results/2008/composer/LRPPI1.tar.gz T. Lidy, A. Rauber, A. Pertusa, P. Peonce de León, J. M. Iñesta 1]<br />
-'''LRPPI2''' = [https://www.music-ir.org/mirex/2008/results/composer/LRPPI2.tar.gz T. Lidy, A. Rauber, A. Pertusa, P. Peonce de Leon, J. M. I├▒esta 2]<br />
+'''LRPPI2''' = [https://www.music-ir.org/mirex/results/2008/composer/LRPPI2.tar.gz T. Lidy, A. Rauber, A. Pertusa, P. Peonce de León, J. M. Iñesta 2]<br />
-'''LRPPI3''' = [https://www.music-ir.org/mirex/2008/results/composer/LRPPI3.tar.gz T. Lidy, A. Rauber, A. Pertusa, P. Peonce de Leon, J. M. I├▒esta 3]<br />
+'''LRPPI3''' = [https://www.music-ir.org/mirex/results/2008/composer/LRPPI3.tar.gz T. Lidy, A. Rauber, A. Pertusa, P. Peonce de León, J. M. Iñesta 3]<br />
-'''LRPPI4''' = [https://www.music-ir.org/mirex/2008/results/composer/LRPPI4.tar.gz T. Lidy, A. Rauber, A. Pertusa, P. Peonce de Leon, J. M. I├▒esta 4]<br />
+'''LRPPI4''' = [https://www.music-ir.org/mirex/results/2008/composer/LRPPI4.tar.gz T. Lidy, A. Rauber, A. Pertusa, P. Peonce de León, J. M. Iñesta 4]<br />
-'''ME1''' = [https://www.music-ir.org/mirex/2008/results/composer/ME1.tar.gz I. M. Mandel, D. P. W. Ellis 1]<br />
+'''ME1''' = [https://www.music-ir.org/mirex/results/2008/composer/ME1.tar.gz I. M. Mandel, D. P. W. Ellis 1]<br />
-'''ME2''' = [https://www.music-ir.org/mirex/2008/results/composer/ME2.tar.gz I. M. Mandel, D. P. W. Ellis 2]<br />
+'''ME2''' = [https://www.music-ir.org/mirex/results/2008/composer/ME2.tar.gz I. M. Mandel, D. P. W. Ellis 2]<br />
-'''ME3''' = [https://www.music-ir.org/mirex/2008/results/composer/ME3.tar.gz I. M. Mandel, D. P. W. Ellis 3]<br />
+'''ME3''' = [https://www.music-ir.org/mirex/results/2008/composer/ME3.tar.gz I. M. Mandel, D. P. W. Ellis 3]<br />
+===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=====
+<csv>2008/composer/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>2008/composer/perClassAccuracy.friedman.detail.csv</csv>
+[[Image:2008_composer.perclassaccuracy.friedman.tukeykramerhsd.png]]
+====Folds vs. Systems====
+The Friedman test was run in MATLAB against the accuracy for each fold.
+=====Friedman's Anova Table=====
+<csv>2008/composer/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>2008/composer/perFoldAccuracy.friedman.detail.csv</csv>
+[[Image:2008_composer.perfoldaccuracy.friedman.tukeykramerhsd.png]]
 [[Category: Results]]

Difference between revisions of "2008:Audio Classical Composer Identification Results"

Latest revision as of 16:07, 23 July 2010

Contents

Introduction

General Legend

Team ID

Overall Summary Results

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

Accuracy Across Folds

Accuracy Across Categories

MIREX 2008 Audio Classical Composer Classification Evaluation Logs and Confusion Matrices

MIREX 2008 Audio Classical Composer Classification Run Times

CSV Files Without Rounding

Results By Algorithm

Friedman's Test for Significant Differences

Classes vs. Systems

Friedman's Anova Table

Tukey-Kramer HSD Multi-Comparison

Folds vs. Systems

Friedman's Anova Table

Tukey-Kramer HSD Multi-Comparison

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