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International Journal of Computer Engineering in Research Trends. Scholarly, Peer-Reviewed,Open Access and Multidisciplinary

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Music Genre Classification Using MFCC, K-NN and SVM Classifier

Nilesh M. Patil, Dr. Milind U. Nemade, , ,
Ph.D Research Scholar1, Pacific Academy of Higher Education and Research University, Udaipur, India.
:10.22362/ijcert/2017/v4/i2/xxxx [UNDER PROCESS]

The audio corpus available today on Internet and Digital Libraries is increasing rapidly in huge volume. We need to properly index them if we want to have access to these audio data. The search engines available in market also find it challenging to classify and retrieve the audio files relevant to the user’s interest. In this paper, we describe an automated classification system model for music genres. We firstly found good feature for each music genre. To obtain feature vectors for the classifiers from the GTZAN genre dataset, features like MFCC vector, chroma frequencies, spectral roll-off, spectral centroid, zero-crossing rate were used. Different classifiers were trained and used to classify, each yielding varying degrees of accuracy in prediction.

Nilesh M. Patil, “Music Genre Classification Using MFCC, K-NN and SVM Classifier”, International Journal Of Computer Engineering In Research Trends, 4(2):43-47, February-2017.

Keywords : Music, MFCC, K-NN, SVM, GTZAN dataset.

[1]	G. Tzanetakis, P. Cook, “Musical genre classification of audio signals”, IEEE Transactions on Speech and Audio Processing, Vol. 10, Issue 5, July 2002.
[2]	Chandsheng Xu, Mc Maddage, Xi Shao, Fang Cao, and Qi Tan, “Musical genre classification using support vector machines”, IEEE Proceedings of International Conference of Acoustics, Speech, and Signal Processing, Vol. 5, pp. V-429-32, 2003.
[3]	N. Scaringella, G. Zoia, and D. Mlynek, “Automatic genre classification of music content: a survey”, IEEE Signal Processing Magazine, Vol. 23, Issue 2, pp. 133–141, 2006.
[4]	Jan Wülfing and Martin Riedmiller, “Unsupervised learning of local features for music classification” ISMIR, pp. 139–144, 2012.
[5] Sox - sound exchange— homepage, 2015.

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