2005:Audio Genre Classification Results
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Contents
Introduction
Goal
To classify polyphonic music audio (in PCM format) into genre categories.
Dataset
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
Overall
| 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
| 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% | -- | -- | [1] |
| 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
| 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 * | ||||
