1. Ponce de León P. J.; Iñesta J. M.; Pérez-Sancho C.

, vol. Pattern Recognition: Progress, Directions and Applications, pp. 41-53. (2006)

Previous work done in genre recognition and characterization from symbolic sources (monophonic melodies extracted from MIDI files) have pointed our research to the use of classifier ensembles to better accomplish the task. This work presents current research in the use of voting ensembles of classifiers trained on statistical description models of melodies, in order to improve both the accuracy and robustness of single classifier systems in the genre recognition task. Different voting schemes are discussed and compared, and results for a corpus of Jazz and Classical music pieces are presented and assesed.