Feature Selection for Multi-Class Genre Classification using Gaussian Mixture Model 


Vol. 32,  No. 10, pp. 965-974, Oct.  2007


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  Abstract

In this paper, we proposed the feature selection algorithm for multi-class genre classification. In our proposed algorithm, we developed GMM separation score based on Gaussian mixture model for measuring separability between two genres. Additionally, we improved feature subset selection algorithm based on sequential forward selection for multi-class genre classification. Instead of setting criterion as entire genre separability measures, we set criterion as worst genre separability measure for each sequential selection step. In order to assess the performance proposed algorithm, we extracted various features which represent characteristics such as timbre, rhythm, pitch and so on. Then, we investigate classification performance by GMM classifier and k-NN classifier for selected features using conventional algorithm and proposed algorithm. Proposed algorithm showed improved performance in classification accuracy up to 10 percent for classification experiments of low dimension feature vector especially.

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  Cite this article

[IEEE Style]

S. Moon, T. Choi, Y. Park, D. H. Youn, "Feature Selection for Multi-Class Genre Classification using Gaussian Mixture Model," The Journal of Korean Institute of Communications and Information Sciences, vol. 32, no. 10, pp. 965-974, 2007. DOI: .

[ACM Style]

Sun-Kuk Moon, Tack-Sung Choi, Young-Cheol Park, and Dae Hee Youn. 2007. Feature Selection for Multi-Class Genre Classification using Gaussian Mixture Model. The Journal of Korean Institute of Communications and Information Sciences, 32, 10, (2007), 965-974. DOI: .

[KICS Style]

Sun-Kuk Moon, Tack-Sung Choi, Young-Cheol Park, Dae Hee Youn, "Feature Selection for Multi-Class Genre Classification using Gaussian Mixture Model," The Journal of Korean Institute of Communications and Information Sciences, vol. 32, no. 10, pp. 965-974, 10. 2007.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880