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Tacksung CHOI†a) , Sunkuk MOON †b) , Young-cheol PARK † †c) , Dae-hee YOUN †d) , and Seokpil LEE † † †e) , Nonmembers SUMMARYIn this paper, we propose a new feature selection algorithm for multi-class classification. The proposed algorithm is based on Gaussian mixture models (GMMs) of the features, and it uses the distance between the two least separable classes as a metric for feature selection. The proposed system was tested with a support vector machine (SVM) for multi-class classification of music. Results show that the proposed feature selection scheme is superior to conventional schemes.