Learning Naive Bayes Classifiers for Music Classification and Retrieval

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.