Combination of homogeneous classifiers for musical genre classification

Content-based music genre classification is a useful tool for multimedia indexing and retrieval. In this paper a novel content-based music genre classification approach that employs combination of homogeneous classifiers is proposed. First, musical surface features and beat-related features are extracted from different pans of music tracks and three 15-dimensional feature vectors are generated. The features are extracted from the beginning, middle and end parts of the music. These features vectors are used to train three multilayer perceptron neural network classifiers. At the classification step, the outputs provided by each neural network based classifier are combined using max, sum and product rules. Experimental results show that the proposed combination of homogeneous classifiers outperforms single feature vectors and single classifiers, achieving higher correct music genre classification rates.