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Enhanced polyphonic music genre classification using high level features

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2 Author(s)
Arash Foroughmand Arabi ; Faculty of Information Technology, Monash University, Melbourne, Australia ; Guojun Lu

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.

Published in:

Signal and Image Processing Applications (ICSIPA), 2009 IEEE International Conference on

Date of Conference:

18-19 Nov. 2009