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Music Genre Classification based on Musical Features of Representative Segments  

Lee, Jong-In (금오공과대학교 소프트웨어공학)
Kim, Byeong-Man (금오공과대학교 컴퓨터공학부)
Publication Information
Journal of KIISE:Software and Applications / v.35, no.11, 2008 , pp. 692-700 More about this Journal
Abstract
In some previous works on musical genre classification, human experts specify segments of a song for extracting musical features. Although this approach might contribute to performance enhancement, it requires manual intervention and thus can not be easily applied to new incoming songs. To extract musical features without the manual intervention, most of recent researches on music genre classification extract features from a pre-determined part of a song (for example, 30 seconds after initial 30 seconds), which may cause loss of accuracy. In this paper, in order to alleviate the accuracy problem, we propose a new method, which extracts features from representative segments (or main theme part) identified by structure analysis of music piece. The proposed method detects segments with repeated melody in a song and selects representative ones among them by considering their positions and energies. Experimental results show that the proposed method significantly improve the accuracy compared to the approach using a pre-determined part.
Keywords
Music Genre Classification System; Music Representative Part Detection; Musical Structure Analysis; Music Segment Detection; Content-based Music Feature Extraction;
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