Learning Naive Bayes Classifiers for Music Classification and Retrieval

In this paper, we explore the use of naive Bayes classifiers for music classification and retrieval. The motivation is to employ all audio features extracted from local windows for classification instead of just using a single song-level feature vector produced by compressing the local features. Two...

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Bibliographic Details
Published in2010 20th International Conference on Pattern Recognition pp. 4589 - 4592
Main Authors Zhouyu Fu, Guojun Lu, Kai Ming Ting, Dengsheng Zhang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2010
Subjects
Artificial neural networks
Equations
Feature extraction
Music Classification
Naive Bayes
Nearest neighbor searches
Niobium
Support vector machines
Training
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Summary:In this paper, we explore the use of naive Bayes classifiers for music classification and retrieval. The motivation is to employ all audio features extracted from local windows for classification instead of just using a single song-level feature vector produced by compressing the local features. Two variants of naive Bayes classifiers are studied based on the extensions of standard nearest neighbor and support vector machine classifiers. Experimental results have demonstrated superior performance achieved by the proposed naive Bayes classifiers for both music classification and retrieval as compared to the alternative methods.
ISBN:1424475422
9781424475421
ISSN:1051-4651
2831-7475
DOI:10.1109/ICPR.2010.1121