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Audio Music Genre Classification Using Different Classifiers and Feature Selection Methods

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2 Author(s)
Yaslan, Y. ; Dept. of Comput. Eng., Istanbul Tech. Univ. ; Cataltepe, Z.

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 plusmn 4.2% on 10 genre set of 1000 music pieces. This performance is better than 71.1 plusmn 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

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Pattern Recognition, 2006. ICPR 2006. 18th International Conference on  (Volume:2 )

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