Content-based music genre classification using timbral feature vectors and support vector machine

Authors:
Shi-Huang Chen;Shih-Hao Chen
Affiliations:
Shu-Te University, Kaohsiung County, Taiwan;I-Shou University, Kaohsiung County, Taiwan
Venue:
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
Year:
2009

Citing 2
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Abstract

This paper proposes a novel content-based music genre classification method using timbral feature vectors and support vector machine (SVM). The timbral feature vectors used in the proposed method consist of both the long-term and the short-term features which can represent the time-varying behavior of music. These features are mel-frequency cepstral coefficient (MFCC) plus log energy with different frame length. The timbral feature vectors will be applied to train an optimized non-linear decision rule for music genre classifier via SVM. This paper selects nine kinds of different music, including classical, jazz, dance, lullaby, country, Bossa Nova, piano, blue note, and hip-hop, for performance evaluation. Experimental results show that the proposed method can achieve the average accuracy rate of 86% for the nice music genres classification.