Music genre classification based on local feature selection using a self-adaptive harmony search algorithm

Abstract

This paper proposes an automatic music genre-classification system based on a local feature-selection strategy by using a self-adaptive harmony search (SAHS) algorithm. First, five acoustic characteristics (i.e., intensity, pitch, timbre, tonality, and rhythm) are extracted to generate an original feature set. A feature-selection model using the SAHS algorithm is then employed for each pair of genres, thereby deriving the corresponding local feature set. Finally, each one-against-one support vector machine (SVM) classifier is fed with the corresponding local feature set, and the majority voting method is used to classify each musical recording. Experiments on the GTZAN dataset were conducted, demonstrating that our method is effective. The results show that the local-selection strategies using wrapper and filter approaches ranked first and third in performance among all relevant methods.