Musical genre classification using support vector machines

Abstract

Automatic musical genre classification is very useful for music indexing and retrieval. In this paper, an efficient and effective automatic musical genre classification approach is presented. A set of features is extracted and used to characterize music content. A multi-layer classifier based on support vector machines is applied to musical genre classification. Support vector machines are used to obtain the optimal class boundaries between different genres of music by learning from training data. Experimental results of multi-layer support vector machines illustrate good performance in musical genre classification and are more advantageous than traditional Euclidean distance based method and other statistic learning methods.