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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. © 2013 Springer-Verlag Berlin Heidelberg.

Original publication

DOI

10.1007/978-3-642-38989-4_26

Type

Conference paper

Publication Date

29/11/2013

Volume

7914 LNCS

Pages

254 - 263