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Automatic genre classification of music content: a survey

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3 Author(s)
N. Scaringella ; Inst. of Signal Process., Ecole Polytech. Fed. de Lausanne, Switzerland ; G. Zoia ; D. Mlynek

This paper reviews the state-of-the-art in automatic genre classification of music collections through three main paradigms: expert systems, unsupervised classification, and supervised classification. The paper discusses the importance of music genres with their definitions and hierarchies. It also presents techniques to extract meaningful information from audio data to characterize musical excerpts. The paper also presents the results of new emerging research fields and techniques that investigate the proximity of music genres

Published in:

IEEE Signal Processing Magazine  (Volume:23 ,  Issue: 2 )