Title

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

Research Areas

Data Management and Analytics

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

Additional URL

http://dx.doi.org/10.1007/11408079_24