A class centric feature and classifier ensemble selection approach for music genre classification
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
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We examine performance of different classifiers on different audio feature sets to determine the genre of a given music piece. For each classifier, we also evaluate performances of feature sets obtained by dimensionality reduction methods. Finally, we experiment on increasing classification accuracy by combining different classifiers. Using a set of different classifiers, we first obtain a test genre classification accuracy of around 79.6 卤 4.2% on 10 genre set of 1000 music pieces. This performance is better than 71.1 卤 7.3% which is the best that has been reported on this data set. We also obtain 80% classification accuracy by using dimensionality reduction or combining different classifiers. We observe that the best feature set depends on the classifier used.