A Comparative Study On Content-based Music Genre Classification

Abstract

Content-based music genre classification is a fundamental component of music information retrieval systems and has been gaining importance and enjoying a growing amount of attention with the emergence of digital music on the Internet. Currently little work has been done on automatic music genre classification, and in addition, the reported classification accuracies are relatively low. This paper proposes a new feature extraction method for music genre classification, <i>DWCHs</i>. <i>DWCHs</i> stands for Daubechies Wavelet Coefficient Histograms. <i>DWCHs</i> capture the local and global information of music signals simultaneously by computing histograms on their Daubechies wavelet coefficients. Effectiveness of this new feature and of previously studied features are compared using various machine learning classification algorithms, including Support Vector Machines and Linear Discriminant Analysis. It is demonstrated that the use of <i>DWCHs</i> significantly improves the accuracy of music genre classification.