Abstract
Scientists have used many different classification methods to solve the problem of music classification. But the efficiency of each classification is different. In this paper, we propose two compared methods on the task of music style classification. More specifically, feature extraction for representing timbral texture, rhythmic content and pitch content are proposed. Comparative evaluations on performances of two classifiers were conducted for music classification with different composers' styles. The result shows that XGB (eXtreme Gradient Boosting) is better suited for small datasets than BPNN (Back Propagation Neural Network).