Music genre classification using dynamic selection of ensemble of classifiers

This paper presents a dynamic ensemble selection method for music genre classification which employs two pools of diverse classifiers. The pools of classifiers are created by using different features types extracted from three distinct segments of each music piece. From these initial pools of weak classifiers, ensembles of classifiers are dynamically selected for each test pattern using the k-nearest oracles method. The experiments compare the performance of different selection strategies on the Latin Music Database to those related to the use of best single classifier, and to the combination of all classifiers in the pool. It was possible to observe that the most promising selection strategy evaluated allows improving the classification accuracy from 63.71% to 70.31%.