On Efficient Music Genre Classification

• Main Authors: SHEN, Jialie, John, Shepherd, Ahh, Ngu
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2005
• Subjects: Music Classification; Genre; Human Factor; Databases and Information Systems; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/1235; http://dx.doi.org/10.1007/11408079_24
• Institution: Singapore Management University

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.