On Efficient Music Genre Classification
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...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | text |
| Language: | English |
| Published: |
Institutional Knowledge at Singapore Management University
2005
|
| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/sis_research/1235 http://dx.doi.org/10.1007/11408079_24 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Singapore Management University |
| Language: | English |
| Summary: | 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. |
|---|