Automatic music genres classification as a pattern recognition problem

24 December 2013 Automatic music genres classification as a pattern recognition problem

Ihtisham Ul Haq, Fauzia Khan, Sana Sharif, Arsalan Shaukat

Proceedings Volume 9067, Sixth International Conference on Machine Vision (ICMV 2013); 90672A (2013) https://doi.org/10.1117/12.2051595
Event: Sixth International Conference on Machine Vision (ICMV 13), 2013, London, United Kingdom

Abstract

Music genres are the simplest and effect descriptors for searching music libraries stores or catalogues. The paper compares the results of two automatic music genres classification systems implemented by using two different yet simple classifiers (K-Nearest Neighbor and Naïve Bayes). First a 10-12 second sample is selected and features are extracted from it, and then based on those features results of both classifiers are represented in the form of accuracy table and confusion matrix. An experiment carried out on test 60 taken from middle of a song represents the true essence of its genre as compared to the samples taken from beginning and ending of a song. The novel techniques have achieved an accuracy of 91% and 78% by using Naïve Bayes and KNN classifiers respectively.

Citation Download Citation

Ihtisham Ul Haq, Fauzia Khan, Sana Sharif, and Arsalan Shaukat "Automatic music genres classification as a pattern recognition problem", Proc. SPIE 9067, Sixth International Conference on Machine Vision (ICMV 2013), 90672A (24 December 2013); https://doi.org/10.1117/12.2051595

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