Automatic music genre classification has long been an important problem. However, there is a paucity of literature that addresses the issue, and in addition, reported accuracy is fairly low. In this paper, we present empirical study of a novel music descriptor generation method for efficient content based music genre classification. Analysis and empirical evidence demonstrate that our approach outperforms state-of-the-art approaches in the areas including accuracy of genre classification with various machine learning algorithms, efficiency on training process. Furthermore, its effectiveness is robust against various kinds of audio alternation. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Shen, J., Shepherd, J., & Ngu, A. H. H. (2005). On efficient music genre classification. In Lecture Notes in Computer Science (Vol. 3453, pp. 253–264). Springer Verlag. https://doi.org/10.1007/11408079_24
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