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2005:Audio Genre Classification Results

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Introduction[edit]

Goal[edit]

To classify polyphonic music audio (in PCM format) into genre categories.

Dataset[edit]

Two sets of data were used: Magnatune and USPOP. The Magnatune dataset has a hierarchical genre taxonomy, while the USPOP categories are at a single level. The audio sampling rates used were either 44.1 KHz or 22.05 KHz (mono). More data information is in the following table:

Dataset Size (@ 44.1 KHz) Number of Training Files Number of Testing Files
Magnatune 34.3 GB 1005 510
USPOP 28.4 GB 940 474

Results[edit]

Overall[edit]

OVERALL
Rank Participant Mean of Magnatune Hierarchical Classification Accuracy
and USPOP Raw Classification Accuracy
1 Bergstra, Casagrande & Eck (2) 82.34%
2 Bergstra, Casagrande & Eck (1) 81.77%
3 Mandel & Ellis 78.81%
4 West, K. 75.29%
5 Lidy & Rauber (SSD+RH) 75.27%
6 Pampalk, E. 75.14%
7 Lidy & Rauber (RP+SSD) 74.78%
8 Lidy & Rauber (RP+SSD+RH) 74.58%
9 Scaringella, N. 73.11%
10 Ahrendt, P. 71.55%
11 Burred, J. 62.63%
12 Soares, V. 60.98%
13 Tzanetakis, G. 60.72%

Magnatune Dataset[edit]

Magnatune Dataset
Rank Participant Hierarchical Classification Accuracy Normalized Hierarchical Classification Accuracy Raw Classification Accuracy Normalized Raw Classification Accuracy Runtime (s) Machine Confusion Matrix Files
1 Bergstra, Casagrande & Eck (2) 77.75% 73.04% 75.10% 69.49% -- -- BCE_2_MTeval.txt
2 Bergstra, Casagrande & Eck (1) 77.25% 72.13% 74.71% 68.73% 23400 B0 BCE_1_MTeval.txt
3 Mandel & Ellis 71.96% 69.63% 67.65% 63.99% 8729 R ME_MTeval.txt
4 West, K. 71.67% 68.33% 68.43% 63.87% 43327 B4 W_MTeval.txt
5 Lidy & Rauber (RP+SSD) 71.08% 70.90% 67.65% 66.85% 6372 B1 LR_RP+SSD_MTeval.txt
6 Lidy & Rauber (RP+SSD+RH) 70.88% 70.52% 67.25% 66.27% 6372 B1 LR_RP+SSD+RH_MTeval.txt
7 Lidy & Rauber (SSD+RH) 70.78% 69.31% 67.65% 65.54% 6372 B1 LR_SSD+RH_MTeval.txt
8 Scaringella, N. 70.47% 72.30% 66.14% 67.12% 22740 G SN_MTeval.txt
9 Pampalk, E. 69.90% 70.91% 66.47% 66.26% 3312 B0 P_MTeval.txt
10 Ahrendt, P. 64.61% 61.40% 60.98% 57.15% 4920 B1 A_MTeval.txt
11 Burred, J. 59.22% 61.96% 54.12% 55.68% 12483 B2 B_MTeval.txt
12 Tzanetakis, G. 58.14% 53.47% 55.49% 50.39% 1312 B0 T_MTeval.txt
13 Soares, V. 55.29% 60.73% 49.41% 53.54% 23880 Y SV_MTeval.txt
14 Li, M. TO * -- -- -- -- -- --
15 Chen & Gao DNC * -- -- -- -- -- --

USPOP Dataset[edit]

USPOP Dataset
Rank Participant Raw Classification Accuracy Normalized Raw Classification Accuracy Runtime (s) Machine Confusion Matrix Files
1 Bergstra, Casagrande & Eck (2) 86.92% 82.91% BCE_2_USeval.txt
2 Bergstra, Casagrande & Eck (1) 86.29% 82.50% 23400 B0 BCE_1_USeval.txt
3 Mandel & Ellis 85.65% 76.91% 7856 R ME_USeval.txt
4 Pampalk, E. 80.38% 78.74% 3090 B0 P_USeval.txt
5 Lidy & Rauber (SSD+RH) 79.75% 75.45% 5164 B1 LR_SSD+RH_USeval.txt
6 West, K. 78.90% 74.67% 18557 B4 W_USeval.txt
7 Lidy & Rauber (RP+SSD) 78.48% 77.62% 5164 B1 LR_RP+SSD_USeval.txt
8 Ahrendt, P. 78.48% 73.23% 9702 B1 A_USeval.txt
9 Lidy & Rauber (RP+SSD+RH) 78.27% 76.84% 5194 B1 LR_RP+SSD+RH_USeval.txt
10 Scaringella, N. 75.74% 77.67% 24606 G SN_USeval.txt
11 Soares, V. 66.67% 67.28% 14369 Y SV_USeval.txt
12 Burred, J. 66.03% 72.50% 9233 B2 B_USeval.txt
13 Tzanetakis, G. 63.29% 50.19% 1320 B0 T_USeval.txt
14 Chen & Gao 22.93% 17.96% N/A Y CG_USeval.txt
15 Li, M. TO *