- Title
- A survey of audio-based music classification and annotation
- Creator
- Fu, Zhouyu; Lu, Guojun; Ting, Kaiming; Zhang, Dengsheng
- Date
- 2011
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/33246
- Identifier
- vital:6360
- Identifier
-
https://doi.org/10.1109/TMM.2010.2098858
- Identifier
- ISSN:1520-9210
- Abstract
- Music information retrieval (MIR) is an emerging research area that receives growing attention from both the research community and music industry. It addresses the problem of querying and retrieving certain types of music from large music data set. Classification is a fundamental problem in MIR. Many tasks in MIR can be naturally cast in a classification setting, such as genre classification, mood classification, artist recognition, instrument recognition, etc. Music annotation, a new research area in MIR that has attracted much attention in recent years, is also a classification problem in the general sense. Due to the importance of music classification in MIR research, rapid development of new methods, and lack of review papers on recent progress of the field, we provide a comprehensive review on audio-based classification in this paper and systematically summarize the state-of-the-art techniques for music classification. Specifically, we have stressed the difference in the features and the types of classifiers used for different classification tasks. This survey emphasizes on recent development of the techniques and discusses several open issues for future research.
- Relation
- IEEE Transactions on Multimedia Vol. 13, no. 2 (2011), p. 303-319
- Rights
- Copyright The Institute of Electrical and Electronics Engineers
- Rights
- This metadata is freely available under a CCO license
- Subject
- 08 Information and Computing Sciences; 09 Engineering; Audio signal processing; Music; Pattern classification; Query processing; Signal classification
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