Music Genre Classification Using Frequency Domain Features

Automatic music genre classification is an active and most popular area of research in Music Information Retrieval (MIR) domain. Such classification enables organized storage and retrieval of music information from a large collection. In this work, we have utilized spectral features that represents important musical properties like timbre, tonality and pitch which play important role in discriminating the genres. For classification, support vector machine and random forest are considered. Experiment is carried out on three different benchmark dataset. Result shows that presented features perform satisfactorily for both the classifiers. Comparison with reported systems reflect the superiority of proposed system. Selection of the feature set is well justified as improved performance is achieved across the classifier and dataset.