- Title
- Enhanced polyphonic music genre classification using high level features
- Creator
- Arabi, Arash; Lu, Guojun
- Date
- 2009
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/71364
- Identifier
- vital:6735
- Identifier
-
https://doi.org/10.1109/ICSIPA.2009.5478635
- Identifier
- ISBN:9781424455621
- Abstract
- The task of classifying the genre of polyphonic music signals is traditionally done using only low level features of the signal. In this paper high level features have been applied to improve the task of music genre classification. The use of statistical chord features and chord progression information in conjunction with low level features are proposed in this paper. The chord progression information is manifested in genre probability descriptors calculated using a pattern matching algorithm. Our proposed method provides an improvement of 12.4% in the classification results over a commonly compared technique.
- Publisher
- IEEE Signal Processing Society
- Relation
- Proceedings of the 2009 IEEE International Conference on Signal and Image Processing Applications p. 1-6
- Rights
- Copyright IEEE Signal Processing Society
- Rights
- This metadata is freely available under a CCO license
- Subject
- Audio signal processing; Music; Pattern matching; Probability; Signal classification; Statistical analysis; 0801 Artificial Intelligence and Image Processing
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