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Classification of Music Data using Fuzzy c-Means with Divergence Kernel  

Park, Dong-Chul (Dept. of Information Eng., Myong Ji University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea CI / v.46, no.3, 2009 , pp. 1-7 More about this Journal
Abstract
An approach for the classification of music genres using a Fuzzy c-Means(FcM) with divergence-based kernel is proposed and presented in this paper. The proposed model utilizes the mean and covariance information of feature vectors extracted from music data and modelled by Gaussian Probability Density Function (GPDF). Furthermore, since the classifier utilizes a kernel method that can convert a complicated nonlinear classification boundary to a simpler linear one, he classifier can improve its classification accuracy over conventional algorithms. Experiments and results on collected music data sets demonstrate hat the proposed classification scheme outperforms conventional algorithms including FcM and SOM 17.73%-21.84% on average in terms of classification accuracy.
Keywords
Classification model; Clustering algorithm; Music genre; Fuzzy c-Means; Self-Organizing Map;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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