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Proceedings Paper

Music genre classification using a hierarchical long short term memory (LSTM) model
Author(s): Chun Pui Tang; Ka Long Chui; Ying Kin Yu; Zhiliang Zeng; Kin Hong Wong
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Paper Abstract

This paper examines the application of Long Short Term Memory (LSTM) model in music genre classification. We explore two different approaches in the paper. (1) In the first method, we use one single LSTM to directly classify 6 different genres of music. The method is implemented and the results are shown and discussed. (2) The first approach is only good for 6 or less genres. So in the second approach, we adopt a hierarchical divide- and-conquer strategy to achieve 10 genres classification. In this approach, music is classified into strong and mild genre classes. Strong genre includes hiphop, metal, pop, rock and reggae because usually they have heavier and stronger beats. The mild class includes jazz, disco, country, classic and blues because they tend to be softer musically. We further divide the sub-classes into sub-subclasses to help with the classification. Firstly, we classify an input piece into strong or mild class. Then for each subclass, we further classify them until one of the ten final classes is identified. For the implementation, each subclass classification module is implemented using a LSTM. Our hierarchical divide-and-conquer idea is built and tested. The average classification accuracy of this approach for 10-genre classification is 50.00%, which is higher than the state-of-the-art approach that uses a single convolutional neural network. From our experimental results, we show that this hierarchical scheme improves the classification accuracy significantly.

Paper Details

Date Published: 26 July 2018
PDF: 7 pages
Proc. SPIE 10828, Third International Workshop on Pattern Recognition, 108281B (26 July 2018); doi: 10.1117/12.2501763
Show Author Affiliations
Chun Pui Tang, The Chinese Univ. of Hong Kong (Hong Kong, China)
Ka Long Chui, The Chinese Univ. of Hong Kong (Hong Kong, China)
Ying Kin Yu, The Chinese Univ. of Hong Kong (Hong Kong, China)
Zhiliang Zeng, The Chinese Univ. of Hong Kong (Hong Kong, China)
Kin Hong Wong, The Chinese Univ. of Hong Kong (Hong Kong, China)

Published in SPIE Proceedings Vol. 10828:
Third International Workshop on Pattern Recognition
Xudong Jiang; Zhenxiang Chen; Guojian Chen, Editor(s)

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