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Musical genre classification is put into context by explaining about the structures in music and how it is analyzed and perceived by humans. The increase of the music databases on the personal collection and the Internet has brought a great demand for music information retrieval, and especially automatic musical genre classification. In this research we focused on combining information from the audio signal than different sources. This paper presents a comprehensive machine learning approach to the problem of automatic musical genre classification using the audio signal. The proposed approach uses two feature vectors, Support vector machine classifier with polynomial kernel function and machine learning algorithms. More specifically, two feature sets for representing frequency domain, temporal domain, cepstral domain and modulation frequency domain audio features are proposed. Using our proposed features SVM act as strong base learner in AdaBoost, so its performance of the SVM classifier cannot improve using boosting method. The final genre classification is obtained from the set of individual results according to a weighting combination late fusion method and it outperformed the trained fusion method. Music genre classification accuracy of 78% and 81% is reported on the GTZAN dataset over the ten musical genres and the ISMIR2004 genre dataset over the six musical genres, respectively. We observed higher classification accuracies with the ensembles, than with the individual classifiers and improvements of the performances on the GTZAN and ISMIR2004 genre datasets are three percent on average. This ensemble approach show that it is possible to improve the classification accuracy by using different types of domain based audio features.

Automatic Music Genre Classification of Audio Signals with Machine Learning Approaches