Difference between revisions of "2005:Symbolic Genre Classification Results"
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===Overall=== | ===Overall=== | ||
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! colspan="4" | OVERALL | ! colspan="4" | OVERALL | ||
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|[https://www.music-ir.org/mirex/abstracts/2005/mckay.pdf McKay & Fujinaga] | |[https://www.music-ir.org/mirex/abstracts/2005/mckay.pdf McKay & Fujinaga] | ||
| − | |77.17% | + | |align="right " | 77.17% |
| − | |65.28% | + | |align="right " | 65.28% |
|---- | |---- | ||
|2 | |2 | ||
|[https://www.music-ir.org/mirex/abstracts/2005/basili.pdf Basili, Serafini, & Stellato (NB)] | |[https://www.music-ir.org/mirex/abstracts/2005/basili.pdf Basili, Serafini, & Stellato (NB)] | ||
| − | |72.08% | + | |align="right " | 72.08% |
| − | |58.53% | + | |align="right " | 58.53% |
|---- | |---- | ||
|3 | |3 | ||
|Li, M. | |Li, M. | ||
| − | |67.57% | + | |align="right " | 67.57% |
| − | |55.90% | + | |align="right " | 55.90% |
|---- | |---- | ||
|4 | |4 | ||
|[https://www.music-ir.org/mirex/abstracts/2005/basili.pdf Basili, Serafini, & Stellato (J48)] | |[https://www.music-ir.org/mirex/abstracts/2005/basili.pdf Basili, Serafini, & Stellato (J48)] | ||
| − | |67.14% | + | |align="right " | 67.14% |
| − | |53.14% | + | |align="right " | 53.14% |
|---- | |---- | ||
|5 | |5 | ||
|[https://www.music-ir.org/mirex/abstracts/2005/ponce.pdf Ponce de Leon & Inesta] | |[https://www.music-ir.org/mirex/abstracts/2005/ponce.pdf Ponce de Leon & Inesta] | ||
| − | |37.76% | + | |align="right " | 37.76% |
| − | |26.52% | + | |align="right " | 26.52% |
|---- | |---- | ||
|} | |} | ||
Revision as of 17:11, 2 August 2010
Introduction
Goal
To classify MIDI recordings into genre categories.
Dataset
Two sets of genre categories were used, one consisting of 38 categories and one consisting of 9 categories. Each category was represented by 25 MIDI files.Thus, the 38 genre test contained 950 MIDI files and the 9 genre test contained 225 MIDI files.Test runs were 3-fold cross validated with each algorithm tested using identical training and testing data splits.
Results
Overall
| OVERALL | |||
|---|---|---|---|
| Rank | Participant | Mean Hierarchical Classification Accuracy | Mean Raw Classification Accuracy |
| 1 | McKay & Fujinaga | 77.17% | 65.28% |
| 2 | Basili, Serafini, & Stellato (NB) | 72.08% | 58.53% |
| 3 | Li, M. | 67.57% | 55.90% |
| 4 | Basili, Serafini, & Stellato (J48) | 67.14% | 53.14% |
| 5 | Ponce de Leon & Inesta | 37.76% | 26.52% |
38 Classes
| 38 Classes | ||||||||
|---|---|---|---|---|---|---|---|---|
| Rank | Participant | Hierarchical Classification Accuracy | Hierarchical Classification Accuracy Std | Raw Classification Accuracy | Raw Classification Accuracy Std | Runtime (s) | Machine | Confusion Matrix Files |
| 1 | McKay & Fujinaga | 64.33% | 1.04 | 46.11% | 1.51 | 3 days | R | MF_38eval.txt |
| 2 | Basili, Serafini, & Stellato (NB) | 62.60% | 0.26 | 45.05% | 0.55 | N/A | N/A | BST_NB_38eval.txt |
| 3 | Basili, Serafini, & Stellato (J48) | 57.61% | 1.14 | 40.95% | 1.35 | N/A | N/A | BST_J48_38eval.txt |
| 4 | Li, M. | 54.91% | 0.66 | 39.79% | 0.87 | 15,948 | G | L_38eval.txt |
| 5 | Ponce de Leon & Inesta | 24.84% | 1.40 | 15.26% | 1.13 | 821 | L | PI_38eval.txt |
9 Classes
| 9 Classes | ||||||||
|---|---|---|---|---|---|---|---|---|
| Rank | Participant | Hierarchical Classification Accuracy | Hierarchical Classification Accuracy Std | Raw Classification Accuracy | Raw Classification Accuracy Std | Runtime (s) | Machine | Confusion Matrix Files |
| 1 | McKay & Fujinaga | 90.00% | 0.60 | 84.44% | 1.41 | 18,375 | R | MF_9eval.txt |
| 2 | Basili, Serafini, & Stellato (NB) | 81.56% | 0.76 | 72.00% | 0.88 | N/A | N/A | BST_NB_9eval.txt |
| 3 | Li, M. | 80.22% | 1.47 | 72.00% | 2.31 | 3,777 | G | L_9eval.txt |
| 4 | Basili, Serafini, & Stellato (J48) | 76.67% | 1.11 | 65.33% | 1.65 | N/A | N/A | BST_J48_9eval.txt |
| 5 | Ponce de Leon & Inesta | 50.67% | 1.26 | 37.78% | 2.30 | 197 | L | PI_9eval.txt |
