Supervised Learning Approach for Classification of Sri Lankan Music based on Music Structure Similarity

DOI: 10.5176/2251-1679_CGAT16.25

Authors: Rajitha Peiris, Lakshman Jayaratne

Abstract:

The task of automatic music genre classification is a research area that is becoming highly popular. Most researchers have been focusing on combining information from different sources than the musical signal. This paper presents a novel approach for the automatic music genre classification problem using audio signal for the context of Sri Lankan Music. The proposed approach uses two feature vectors, Support Vector Machine (SVM) classifier with radialbasis kernel function. More specifically, two feature sets for representing frequency domain, temporal domain, cepstral domain and modulation frequency domain audio features are proposed. The final genre classification is obtained from the set of individual results according to their overall classification accuracy and their recall values. Music genre classification accuracy of 74.5{6e6090cdd558c53a8bc18225ef4499fead9160abd3419ad4f137e902b483c465} was recorded as the highest overall classification accuracy on our dataset containing over 100 songs over the five musical genres. This approach shows that it is possible to implement a genre classification model with a reasonably good accuracy by using different types of domain based audio features.

Keywords: musical genre classification, audio signal analysis, music information retrieval, feature extraction, SVM