Music genre classification: A semi-supervised approach

Poria, Soujanya; Gelbukh, Alexander; Hussain, Amir; Bandyopadhyay, Sivaji; Howard, Newton

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.

• Presentation Conference Type: Conference Paper (Published)
• Conference Name: MCPR 2013: 5th Mexican Conference on Pattern Recognition
• Start Date: Jun 26, 2013
• End Date: Jun 29, 2013
• Publication Date: 2013
• Deposit Date: Oct 11, 2019
• Publisher: Springer
• Pages: 254-263
• Series Title: Lecture Notes in Computer Science
• Series Number: 7914
• Series ISSN: 0302-9743
• Book Title: Pattern Recognition: 5th Mexican Conference, MCPR 2013, Querétaro, Mexico, June 26-29, 2013. Proceedings
• ISBN: 978-3-642-38988-7
• DOI: https://doi.org/10.1007/978-3-642-38989-4_26
• Keywords: Fuzzy Cluster; Audio Signal; Hard Cluster; Music Information Retrieval; Fuzzy Support Vector Machine
• Public URL: http://researchrepository.napier.ac.uk/Output/1793164