Difference between revisions of "2005:Symbolic Genre Classification Results"

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(Introduction)
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==Introduction==
 
==Introduction==
''' Goal:''' To classify MIDI recordings into genre categories.
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===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 crossvalidated with each algorithm tested using identical training and testing data splits.
+
===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==
 
==Results==

Revision as of 17:10, 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