Name
Abstract | ||
|---|---|---|
In this paper we present an approach to music genre classification which converts an audio signal into spectrograms and extracts texture features from these time-frequency images which are then used for modeling music genres in a classification system. The texture features are based on Local Binary Pattern, a structural texture operator that has been successful in recent image classification research. Experiments are performed with two well-known datasets: the Latin Music Database (LMD), and the ISMIR 2004 dataset. The proposed approach takes into account some different zoning mechanisms to perform local feature extraction. Results obtained with and without local feature extraction are compared. We compare the performance of texture features with that of commonly used audio content based features (i.e. from the MARSYAS framework), and show that texture features always outperforms the audio content based features. We also compare our results with results from the literature. On the LMD, the performance of our approach reaches about 82.33%, above the best result obtained in the MIREX 2010 competition on that dataset. On the ISMIR 2004 database, the best result obtained is about 80.65%, i.e. below the best result on that dataset found in the literature. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.sigpro.2012.04.023 | Signal Processing |
Keywords | Field | DocType |
classification system,structural texture operator,lbp textural feature,best result,music genre classification,audio signal,extracts texture feature,audio content,local feature extraction,texture feature,pattern recognition,texture,image processing | Audio signal,Pattern recognition,Spectrogram,Computer science,Local binary patterns,Image processing,Feature extraction,Artificial intelligence,Contextual image classification | Journal |
Volume | Issue | ISSN |
92 | 11 | 0165-1684 |
Citations | PageRank | References |
44 | 1.54 | 20 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Y. M. G. Costa | 1 | 44 | 1.54 |
| L. S. Oliveira | 2 | 385 | 25.17 |
| Alessandro L. Koerich | 3 | 525 | 39.59 |
| Fabien Gouyon | 4 | 258 | 22.84 |
| J. G. Martins | 5 | 58 | 2.65 |