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Music Genre Classification: A Semi-supervised Approach

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNIP,volume 7914)

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.

Keywords

  • Fuzzy Cluster
  • Audio Signal
  • Hard Cluster
  • Music Information Retrieval
  • Fuzzy Support Vector Machine

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Poria, S., Gelbukh, A., Hussain, A., Bandyopadhyay, S., Howard, N. (2013). Music Genre Classification: A Semi-supervised Approach. In: Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F., Rodríguez, J.S., di Baja, G.S. (eds) Pattern Recognition. MCPR 2013. Lecture Notes in Computer Science, vol 7914. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38989-4_26

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  • DOI: https://doi.org/10.1007/978-3-642-38989-4_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38988-7

  • Online ISBN: 978-3-642-38989-4

  • eBook Packages: Computer ScienceComputer Science (R0)