A robust music genre classification approach for global and regional music datasets evaluation

This paper deals with two problems: (1) the selection of a set of music features in order to achieve high genre classification accuracies; (2) the absence of a representative music dataset of regional Brazilian music. In this paper, we propose a set of features to classify genres of music. The features proposed were obtained by a methodical selection of important features used in the literature of Music Information Retrieval (MIR) and Music Emotion Recognition (MER). Besides, we propose a new music dataset called BMD (Brazilian Music Dataset)1, containing 120 songs labeled in 7 musical genres: FoFFó, Rock, Repente, MPB(Música Popular Brasileira — Brazilian Popular Music), Brega, Sertanejo and Disco. An important characteristic of this new dataset compared with others, is the presence of three popular genres in Brazil Northeast region: Repente, Brega and a characteristic genre similar to MPB, which we also call as MPB. We evaluated our proposed features on both datasets: GTZAN and BMD. The proposed approach achieved average accuracy (after 30 runs of 5-fold-cross-validations) of 79.7% for GTZAN and 86.11% for the BMD. Another important contribution of this work is random repetition of cross-validation executions. Most of the papers performs only a single n-fold cross-validation. We criticize that practice and propose, at least, 30 random executions to compute the average accuracy.