Learning naive Bayes classifiers for music classification and retrieval

Title: Learning naive Bayes classifiers for music classification and retrieval
Creator: Fu, Zhouyu; Lu, Guojun; Ting, Kaiming; Zhang, Dengsheng
Date: 2010
Type: Text; Conference paper
Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/61369
Identifier: vital:6358
Identifier: https://doi.org/10.1109/ICPR.2010.1121
Identifier: ISBN:9780769541099
Abstract: 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.
Publisher: Istanbul IEEE Computer Society
Relation: Proceedings of the 20th International Conference on Pattern Recognition p. 4589-4592
Rights: This metadata is freely available under a CCO license
Subject: 0801 Artificial Intelligence and Image Processing
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