The increase in the availability of music over internet has attracted researchers to devise automated tools and techniques for the classification and retrieval of music in an effective manner. In this paper, we propose an approach to automatically classify the monophonic songs or cappella. Each song in the training set is divided into frames and from each frame thirteen MFCC features are extracted. The average and variance of these features are used to represent each song for two different classifiers. These features are used to train classifiers separately which can then assign a suitable class to an unlabeled song. Experiments are conducted on two different datasets to illustrate the effectiveness of the proposed method.

1. Beth Logan, "Mel frequency cepstral coefficients for music modeling", In Proc. of the International Symposium on Music Information Retrieval, 2000.
2. Z. Jun, S. Kwong, W. Gang, Q. Hong, "Using Mel Frequency Cepstral Coefficients in Missing Data Technique", In EURASIP Journal on Applied Signal Processing, Vol. 2004, No. 3, pp. 340-346, 2004.
3. Bokyung Sung, Myung Bum Jung and Ilju Ko, "A Feature Based Music Content Recognition method using Simplified MFCC" International Journal of Principles and Applications of Information Science and Technology, Vol. 2, No. 1, pp. 13-23, July 2008.
4. G. Agostini, Maurizio Longari, Emanuele Pollastri, "Musical Instrument Timbres Classification with Spectral Features", In EURASIP Journal on Applied Signal Processing vol 2003, no. 1, pp 5–14, 2003.
5. Aryafar, Kamelia, Sina Jafarpour, and Ali Shokoufandeh "Music genre classification using sparsity-eager support vector machines", Technical report, Drexel University, 2012.
6. Md. Rashidul Hasan, Mustafa Jamil, Md. Golam Rabbani Md. Saifur Rahman, "Speaker Identification Using Mel Frequency Cepstral Coefficients", In the Proceedings of 3rd International Conference on Electrical & Computer Engineering, ICECE 2004, pp. 565-568, December 2004.
7. Michael I. Mandel and Daniel P. W. Ellis, "Song-Level Features And Support Vector Machines For Music Classification", In the Proceedings of the 6th International Conference on Music Information Retrieval (ISMIR), pp. 594-599, September 2005.
8. Arijit Ghosal, Rudrasis Chakraborty, Bibhas Chandra Dhara and Sanjoy Kumar Saha, "Music Classification based on MFCC Variants and Amplitude Variation pattern: A Hierarchical Approach", In International Journal of Signal Processing, Image Processing and Pattern Recognition Vol. 5, No. 1, pp. 131-150, March, 2012.
9. LiuYongchun, Song Hong, Yang Jing, "Research on Music Classification Based on MFCC and BP Neural Network", In the Proceedings of 2nd International Conference on Information, Electronics and Computer, ICIEAC, pp. 129-132, 2014.
10. Jau-Ling Shih, Chang-Hsing Lee, Shiue-Wei Lin, "Automatic Classification of Musical Audio Signals", In Journal of Information Technology and Applications, Vol. 1 No. 2, pp. 95-105, September, 2006.
11. Loughran, R. , Walker, J. , O'Neill, M. , and O'Farrell, M. , "The Use of Mel-frequency Cepstral Coefficients in Musical Instrument Identification", In the Proceedings of International Computer Music Conference (ICMC), 24-29 August 2008
12. I. Mierswa and K. Morik, "Automatic Feature Extraction for Classifying Audio Data", In Machine Learning Journal, vol. 58, Issue 2-3, pp. 127-149, 2005.
13. F. Gouyon, S. Dixon, E. Pampalk, and G. Widmer, "Evaluating rhythmic descriptors for musical genre classification". In the Proceedings of the AES 25th International Conference, London, UK, June 17-19 2004.
14. Chai, W, and B. Vercoe, "Folk music classification using hidden Markov models", In the Proceedings of the International Conference on Artificial Intelligence, June 2001.
15. F. Fernandez, F. Chavez, R. Alcala, and F. Herrera, "Musical genre classification by means of fuzzy rule-based systems: A preliminary approach", In IEEE Congress on Evolutionary Computing, pp. 2571–2577, 2011.
16. O. Lartillot and P. Toiviainen. MIR in matlab (II): A toolbox for musical feature extraction from audio. In Proceedings of 5th International Conference on Music Information Retrieval, 2007.
17. G. Tzanetakis and P. Cook, "Musical genre classification of audio signals ", In IEEE Transactions on Audio and Speech Processing, Vol. 10, Issue 5, pp. 293-302, 2002.

Monophonic Music Mfcc Classification.