1. Piccoli, H. C. B.; Silla, C. N. Jr.; Ponce de León, P. J.; Pertusa, A.

, vol. Proc. of the 2013 IEEE International Conference on Systems, Man and Cybernetics (SMC), pp. 1901-1905. Manchester, UK (2013)

The automatic music genre classification task is an active area of research in the field of Music Information Retrieval. In this paper we use two different symbolic feature sets for genre classification and combine them using an early fusion approach. Our results show that early fusion achieves better classification accuracy than using any of the individual feature sets. Furthermore, when compared with some of the state of the art approaches using the same experimental conditions, early fusion of symbolic features is ranked the second best method.