On Efficient Music Genre Classification
Conference Proceeding Article
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.
Music Classification, Genre, Human Factor
Databases and Information Systems | Numerical Analysis and Scientific Computing
Data Management and Analytics
Database Systems for Advanced Applications: 10th International Conference, DASFAA 2005, Beijing, China, April 17-20: Proceedings
City or Country
SHEN, Jialie; John, Shepherd; and Ahh, Ngu.
On Efficient Music Genre Classification. (2005). Database Systems for Advanced Applications: 10th International Conference, DASFAA 2005, Beijing, China, April 17-20: Proceedings. 3453, 253-264. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/1235