TRADITIONAL MALAYSIAN MUSICAL GENRES CLASSIFICATION BASED ON THE ANALYSIS OF BEAT FEATURE IN AUDIO

Abstract

Interest on automated genre classification systems is growing following the increase in the number of musical digital data collections. Many of these systems have been researched and developed to classify Western musical genres such as pop, rock or classical. However, adapting these systems for the classification of Traditional Malay Musical (TMM) genres which includes Gamelan, Inang and Zapin, is difficult due to the differences in musical structures and modes. This study investigates the effects of various factors and audio feature set combinations towards the classification of TMM genres. Results from experiments conducted in several phases show that factors such as dataset size, track length and location¸ together with various combinations of audio feature sets comprising Short Time Fourier Transform (STFT), Mel-Frequency Cepstral Coefficients (MFCCs) and Beat Features affect classification. Based on parameters optimized for TMM genres, classification performances were evaluated against three groups of human subjects: experts, trained and untrained. Performances of both machine and human were shown to be comparable.