Evaluation of different audio features for musical genre classification

Abstract

Musical genre classification is an important issue for the music information retrieval system. There are two essential components for music genre classification, which are audio features and classifier. This paper considers various kinds of the features for genre classification related with dynamics, rhythm, spectral, and tonal characteristics of music. In the paper up to the 4th order central moments for different features are considered to evaluate the overall classification accuracy. In addition, Extreme Learning Machine (ELM) with bagging is introduced and compared with well-known Support Vector Machines (SVM) in terms of the overall classification accuracy. Based on the aforementioned features sets and ELM classifier, experiments are performed with well-known datasets: GTZAN with ten different musical genres. Through the experiments we found that some type of features is more important to others and the two classifiers provide comparable results for genre classification.