Musical Genre Classification Using Ensemble of Classifiers

Most automatic music genre classification researches have been focusing on combining information from different sources than the musical signal. This paper presents an ensemble approach for the automatic music genre classification problem using audio signal. The proposed approach uses two feature vectors, Support vector machine classifier with polynomial kernel function and a pattern recognition ensemble approach. More specifically, two feature sets for representing frequency domain, temporal domain, cepstral domain and modulation frequency domain audio features are proposed. The final genre classification is obtained from the set of individual results according to a weighting combination late fusion method. Music genre classification accuracy of 78% is reported on the GTZAN dataset over the ten musical genres. This approach shows that it is possible to improve the classification accuracy by using different types of domain based audio features.