Abstract
The emotional content perceived from music has great impact on human beings. Research related to music is attaining more and more recognition not only in the field of musicology and psychology but also getting attention of engineers and doctors. The categorization of music can be carried out by considering various attributes such as genres, emotional content, mood, instrumental etc. In this work Hindi music signals belonging to four genres - Classical, Folk, Ghazal and Sufi are considered. Music signals belonging to these genres are divided into positive arousal, negative arousal, positive valence and negative valence by considering arousal and valence as parameters. Spectral features are calculated for the music clips using MIR toolbox. The classification is done by using K-nearest neighbor (K-NN), Naive Bayes (NB) and Support vector machine (SVM). The classification process is conducted for all the four genres and also for arousal and valence classes. The accuracy, precision and recall are considered as evaluation parameter in this work. The evaluation parameters of all the genres and classification results of all the classifiers used are compared in the proposed work. Results reveal that SVM classifier outperforms other two classifiers in terms of the parameters considered.
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Chaudhary, D., Singh, N.P., Singh, S. (2019). Genre Based Classification of Hindi Music. In: Abraham, A., Gandhi, N., Pant, M. (eds) Innovations in Bio-Inspired Computing and Applications. IBICA 2018. Advances in Intelligent Systems and Computing, vol 939. Springer, Cham. https://doi.org/10.1007/978-3-030-16681-6_8
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DOI: https://doi.org/10.1007/978-3-030-16681-6_8
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