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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
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
|
