Perceptual feature-based song genre classification using RANSAC

Abstract

In the context of a content-based music retrieval system or archiving digital audio data, genre-based classification of song may serve as a fundamental step. In the earlier attempts, researchers have described the song content by a combination of different types of features. Such features include various frequency and time domain descriptors depicting the signal aspects. Perceptual aspects also have been combined along with. A listener perceives a song mostly in terms of its tempo rhythm, periodicity, pitch and their variation and based on those recognises the genre of the song. Motivated by this observation, in this work, instead of dealing with wide range of features we have focused only on the perceptual aspect like melody and rhythm. In order to do so audio content is described based on pitch, tempo, amplitude variation pattern and periodicity. Dimensionality of descriptor vector is reduced and finally, random sample and consensus RANSAC is used as the classifier. Experimental result indicates the effectiveness of the proposed scheme.