Music Genre Classification: A Semi-supervised Approach

  • Soujanya Poria
  • Alexander Gelbukh
  • Amir Hussain
  • Sivaji Bandyopadhyay
  • Newton Howard
Conference paper

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

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7914)
Cite this paper as:
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

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Soujanya Poria
    • 1
  • Alexander Gelbukh
    • 2
  • Amir Hussain
    • 3
  • Sivaji Bandyopadhyay
    • 1
  • Newton Howard
    • 4
  1. 1.Computer Science and Engineering DepartmentJadavpur UniversityIndia
  2. 2.CICInstituto Politécnico NacionalDF, MexicoMexico
  3. 3.Dept. of Computing Science and MathematicsUniversity of StirlingUnited Kingdom
  4. 4.Brain Science FoundationUSA

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