On Efficient Music Genre Classification
Publication Type
Conference Proceeding Article
Publication Date
2005
Abstract
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.
Keywords
Music Classification, Genre, Human Factor
Discipline
Databases and Information Systems | Numerical Analysis and Scientific Computing
Publication
Database Systems for Advanced Applications: 10th International Conference, DASFAA 2005, Beijing, China, April 17-20: Proceedings
Volume
3453
First Page
253
Last Page
264
ISBN
9783540320050
Identifier
10.1007/11408079_24
Publisher
Springer Verlag
City or Country
Beijing, China
Citation
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.
Available at: https://ink.library.smu.edu.sg/sis_research/1235
Additional URL
http://dx.doi.org/10.1007/11408079_24