Learning Naive Bayes Classifiers for Music Classification and Retrieval

Zhouyu Fu, Guojun Lu, K. Ting, Dengsheng Zhang
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引用次数: 23

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.
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学习朴素贝叶斯分类器用于音乐分类和检索
在本文中,我们探索了朴素贝叶斯分类器在音乐分类和检索中的应用。其动机是使用从局部窗口提取的所有音频特征进行分类,而不是仅仅使用压缩局部特征产生的单个歌曲级别特征向量。基于标准最近邻分类器和支持向量机分类器的扩展,研究了两种不同的朴素贝叶斯分类器。实验结果表明,与其他方法相比,所提出的朴素贝叶斯分类器在音乐分类和检索方面都取得了更好的性能。
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