Abstract
In the context of music information retrieval, genre based classification of song is very important. In this work, we have presented a scheme for automatic classification of song signal into two categories like classical and non-classical/popular song. Strong presence of beat and rhythm in the popular songs forms a distinctive pattern and high frequency sub bands obtained after wavelet decomposition bear the signatures. We have computed MFCC based features corresponding to the decomposed signals. Co-occurrence of individual Mel frequency co-efficient computed over a small period are studied and features are obtained to represent the signal pattern. RANSAC has been utilized as the classifier. Experimental result indicates the effectiveness of the proposed scheme.
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Ghosal, A., Chakraborty, R., Dhara, B.C., Saha, S.K. (2011). Song Classification: Classical and Non-classical Discrimination Using MFCC Co-occurrence Based Features. In: Kim, Th., Adeli, H., Ramos, C., Kang, BH. (eds) Signal Processing, Image Processing and Pattern Recognition. SIP 2011. Communications in Computer and Information Science, vol 260. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27183-0_19
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DOI: https://doi.org/10.1007/978-3-642-27183-0_19
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