Music Genre Classification Using MFCC, K-NN and SVM Classifier
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Abstract
The audio corpus available today on the 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 the 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 features 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, and zero-crossing rate were used. Different classifiers were trained and used to classify, each yielding varying degrees of accuracy in prediction.
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References
Tzanetakis, G., & Cook, P. (2002). Musical genre classification of audio signals. IEEE Transactions on Speech and Audio Processing, 10(5), July 2002.
Xu, C., Maddage, M., Shao, X., Cao, F., & Tan, Q. (2003). Musical genre classification using support vector machines. In Proceedings of International Conference of Acoustics, Speech, and Signal Processing (Vol. 5, pp. V-429-32).
Scaringella, N., Zoia, G., & Mlynek, D. (2006). Automatic genre classification of music content: A survey. IEEE Signal Processing Magazine, 23(2), 133–141.
Wülfing, J., & Riedmiller, M. (2012). Unsupervised learning of local features for music classification. In ISMIR, pp. 139–144.
Sox.sourceforge.net. (2015). Sox - sound exchange—homepage.
Marsyas.info. (n.d.). Downloadable datasets. Retrieved from http://marsyas.info/downloads/datasets.html