Visualized Feature Fusion and Style Evaluation for Musical Genre Analysis

YAO Qingjun; LI Haifeng; SUN Jiayin; MA Lin

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Visualized Feature Fusion and Style Evaluation for Musical Genre Analysis

YAO Qingjun * #,LI Haifeng,SUN Jiayin,MA Lin

School of Computer, Harbin Institute of Technology

*Correspondence author

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Funding: Supported by National Natural Science Foundation of China （No.60772076), Key Laboratory Opening Funding of MOE-Microsoft Key Laboratory of Natural Language Processing and Speech）

Opened online: 9 June 2010

Accepted by: none

Citation: YAO Qingjun,LI Haifeng,SUN Jiayin.Visualized Feature Fusion and Style Evaluation for Musical Genre Analysis[OL]. [ 9 June 2010] http://en.paper.edu.cn/en_releasepaper/content/4375363

Different kinds of features in time domain, spectral domain and cepstral domain are used for musical genre classification. In this paper, through the fusion of short-term timbral features and long-term rhythmic feature, we propose a novel method where: musical genre vector is constructed using the likelihood ratio of GMM (Gaussian Mixture Model) and radar chart is applied to provide visualized style evaluation for musical genre analysis, a promising performance is achieved over our database consisting of seven different types of music. Because of the fuzzy definition of musical genres, we also investigate the music with dual-genre based on musical genre vector and radar chart.

Keywords:musical genre analysis;musical genre vector;beat histogram;feature fusion;GMM;Radar chart

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