Conference Paper (published)

Music Genre Classification: A Semi-supervised Approach

Details

Citation

Poria S, Gelbukh A, Hussain A, Bandyopadhyay S & Howard N (2013) Music Genre Classification: A Semi-supervised Approach. In: Carrasco-Ochoa J, Martinez-Trinidad J, Rodriguez J & di Baja G (eds.) Pattern Recognition: 5th Mexican Conference, MCPR 2013, Querétaro, Mexico, June 26-29, 2013. Proceedings. Lecture Notes in Computer Science, 7914. MCPR 2013 : 5th Mexican Conference on Pattern Recognition, Queretaro, Mexico, 26.06.2013-29.06.2013. Berlin Heidelberg: Springer, pp. 254-263. http://link.springer.com/chapter/10.1007/978-3-642-38989-4_26#; https://doi.org/10.1007/978-3-642-38989-4_26

Abstract
Music genres can be seen as categorical descriptions used to classify music basing on various characteristics such as instrumentation, pitch, rhythmic structure, and harmonic contents. Automatic music genre classification is important for music retrieval in large music collections on the web. We build a classifier that learns from very few labeled examples plus a large quantity of unlabeled data, and show that our methodology outperforms existing supervised and unsupervised approaches. We also identify salient features useful for music genre classification. We achieve 97.1% accuracy of 10-way classification on real-world audio collections.

StatusPublished
FundersEngineering and Physical Sciences Research Council and Sitekit Solutions Ltd
Title of seriesLecture Notes in Computer Science
Number in series7914
Publication date31/12/2013
Publication date online30/06/2013
URLhttp://hdl.handle.net/1893/20596
PublisherSpringer
Publisher URLhttp://link.springer.com/…-642-38989-4_26#
Place of publicationBerlin Heidelberg
ISSN of series0302-9743
ISBN978-3-642-38988-7
ConferenceMCPR 2013 : 5th Mexican Conference on Pattern Recognition
Conference locationQueretaro, Mexico
Dates

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