Automatic Genre Classification Using Fractional Fourier Transform Based Mel Frequency Cepstral Coefficient and Timbral Features

• Autorzy: Bhalke D. G. , Rajesh B. , Bormane D. S.

Identyfikatory

DOI

10.1515/aoa-2017-0024

• Języki publikacji: EN

Abstrakty

EN

This paper presents the Automatic Genre Classification of Indian Tamil Music and Western Music using Timbral and Fractional Fourier Transform (FrFT) based Mel Frequency Cepstral Coefficient (MFCC) features. The classifier model for the proposed system has been built using K-NN (K-Nearest Neighbours) and Support Vector Machine (SVM). In this work, the performance of various features extracted from music excerpts has been analysed, to identify the appropriate feature descriptors for the two major genres of Indian Tamil music, namely Classical music (Carnatic based devotional hymn compositions) & Folk music and for western genres of Rock and Classical music from the GTZAN dataset. The results for Tamil music have shown that the feature combination of Spectral Roll off, Spectral Flux, Spectral Skewness and Spectral Kurtosis, combined with Fractional MFCC features, outperforms all other feature combinations, to yield a higher classification accuracy of 96.05%, as compared to the accuracy of 84.21% with conventional MFCC. It has also been observed that the FrFT based MFCC effieciently classifies the two western genres of Rock and Classical music from the GTZAN dataset with a higher classification accuracy of 96.25% as compared to the classification accuracy of 80% with MFCC.

Słowa kluczowe

EN

feature extraction; Timbral features; MFCC; Mel Frequency Cepstral Coefficient; FrFT; fractional Fourier transform; Fractional MFCC; Tamil Carnatic music

• Wydawca: Instytut Podstawowych Problemów Techniki PAN ; Komitet Akustyki PAN ; Polskie Towarzystwo Akustyczne
• Czasopismo: Archives of Acoustics
• Rocznik: 2017
• Tom: Vol. 42, No. 2
• Strony: 213--222
• Opis fizyczny: Bibliogr. 28 poz., rys., tab., wykr.

Twórcy

autor

Bhalke D. G.

• Deptartment of Electronics & Telecommunication, JSPM’s Rajarshi Shahu College of Engineering, SPPU, Pune, India

autor

Rajesh B.

• Deptartment of Electronics & Telecommunication, JSPM’s Rajarshi Shahu College of Engineering, SPPU, Pune, India

autor

Bormane D. S.

• Deptartment of Electronics & Telecommunication, JSPM’s Rajarshi Shahu College of Engineering, SPPU, Pune, India

Bibliografia

• 1. Ashok Narayanan V., Prabhu K. M. M. (2003), The Fractional Fourier Transform: theory, implementationand error analysis, Microprocessors and Microsystems, 27, 10, 511–521, doi: 10.1016/S0141-9331(03)00113-3.
• 2. Bagul M., Soni D., Saravana Kumar K. (2014), Recognition of similar patterns in popular Hindi Jazz songs by music data mining, International Conference on Contemporary Computing and Informatics (IC3I), pp. 1274–1278, November 27–29, doi: 10.1109/IC3I.2014.7019799.
• 3. Baniya B. K., Ghimire D., Lee J. (2014), A novel approach of automatic music genre classification based on timbral texture and rhythmic content features, 16th International Conference on Advanced Communication Technology (ICACT), pp. 96–102.
• 4. Benetos E., Kotropoulos C. (2010), Non-Negative Tensor Factorization Applied to Music Genre Classification, IEEE Transactions on Audio, Speech, and Language Processing, 18, 8, 1955–1967.
• 5. Bhalke D. G, Rao C. B. R., Bormane D. S. (2014), Musical Instrument classification using higher order Spectra, International Conference on Signal Processing and Integrated Networks (SPIN), pp. 40–45, February 20–21, doi: 10.1109/SPIN.2014.6776918.
• 6. Bhalke D. G., Rao C. B. R., Bormane D. S. (2016), Automatic musical instrument classification using Fractional Fourier Transform based-MFCC features and counter propagation neural network, Journal of Intelligent Information System, 46, 3, 425–446, doi: 10.1007/s10844-015-0360-9.
• 7. Chen S-H., Chen S-H., Truong T-K. (2012), Automatic music genre classification based on wavelet package transform and best basis algorithm, IEEE International Symposium on Circuits and Systems (ISCAS), pp. 3202–3205, May 20–23.
• 8. Chen S-H., Chen S-H., Guido R. C. (2010), Music genre classification algorithm based on dynamic frame analysis and support vector machine, IEEE International Symposium on Multimedia (ISM), pp. 357–361, December 13–15.
• 9. Fu Z., Lu G., Ting K. M., Zhang D. (2011), A Survey of audio-based music classification and annotation, Multimedia IEEE Transactions, 13, 2, 303–319.
• 10. Gaikwad S., Chitre A. V., Dandawate Y. H. (2014), Classification of Indian classical instruments using spectral and principal component analysis based cepstrum features, International Conference on Electronic Systems, Signal Processing and Computing Technologies (ICESC), pp. 276–279, January 9–11.
• 11. Ghosal A., Chakraborty R., Chandra Dhara B., Saha S. K. (2012), Music classification based on MFCC variants and amplitude variation pattern: a hierarchical approach, International Journal of Signal Processing, Image Processing and Pattern Recognition, 5, 1, 131–150.
• 12. Jothilakshmi S., Kathiresan N. (2012), Automatic music genre classification for Indian Music, International Conference on Software and Computer Applications (ICSCA 2012), IPCSIT, Vol. 41, pp. 55–59, IAC-SIT Press, Singapore.
• 13. Kini S., Gulati S., Rao P. (2011), Automatic genre classification of North Indian devotional music, National Conference on Communications (NCC), pp. 1–5, January 28–30, doi: 10.1109/NCC.2011.5734697.
• 14. Krishnaswamy A. (2003), Application of pitch tracking to South Indian classical music, [in:] Proceedings of IEEE International Conference on Acoustics, Speech and Signal (ICASSP ’03), Vol. 5, pp. V-557-60, April 6–10, doi: 10.1109/ICASSP.2003.1200030.
• 15. Kumar V., Pandya H., Jawahar C. V. (2014), Identifying Ragas in Indian music, 22nd International Conference on Pattern Recognition (ICPR), pp. 767–772, August 24–28, doi: 10.1109/ICPR.2014.142.
• 16. Li T., Ogihara M. (2006), Toward intelligent music information retrieval, IEEE Transactions on Multimedia, 8, 3, 564–574.
• 17. Li T., Tzanetakis G. (2003), Factors in automatic musical genre classification of audio signals, IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, pp. 143–146, October 19–22.
• 18. Lim S-C., Lee J-S., Jang S-J., Lee S-P., Kim M. Y. (2012), Music-genre classification system based on spectro-temporal features and feature selection, IEEE Transactions in Consumer Electronics, 58, 4, 1262–1268.
• 19. Meng A., Ahrendt P., Larsen J., Hansen L. K. (2007), Temporal feature integration for music genre classification, IEEE Transactions in Audio, Speech, and Language Processing, 15, 5, 1654-1664.
• 20. Nagavi T. C., Bhajantri N. U. (2011), Overview of automatic Indian music information recognition, classification and retrieval systems, International Conference on Recent Trends in Information Systems (ReTIS), pp. 111–116, December 21–23, doi: 10.1109/ReTIS.2011.6146850.
• 21. Rao P. (2012), Audio metadata extraction: The case for Hindustani classical music, International Conference on Signal Processing and Communications (SPCOM), pp. 1–5, July 22–25, doi: 10.1109/SP-COM.2012.6290243.
• 22. Rosner A., Schuller B., Kostek B. (2014), Classification of music genre based on music separation into harmonic and drum components, Archives of Acoustics, 39, 4, 629–638, doi: 10.2478/aoa-2014-0068.
• 23. Salamon J., Gome E. (2012), Melody extraction from polyphonic music signals using pitch contour characteristics, IEEE Transactions on Audio, Speech, and Language Processing, 20, 6, 1759–1770.
• 24. Scaringella N., Zoia G., Mlynek D. (2006), Automatic genre classification of music content: a survey, IEEE Signal Processing Magazine, 23, 2, 133–141.
• 25. Shao X., Maddage M. C., Changsheng Xu, Kankanhalli M. S. (2005), Automatic music summarization based on music structure analysis, IEEE International Conference on Acoustics, Speech, and Signal Processing, 2, ii/1169–ii/1172, March 18–23, doi: 10.1109/ICASSP.2005.1415618.
• 26. Tamil Music, http://www.carnatica.net/tmusic.htm (access on February 2011).
• 27. Tzanetakis G., Cook P. (2002), Musical genre classification of audio signals, IEEE Transactions on Speech and Audio Processing, 10, 5, 293–302, doi: 10.1109/TSA.2002.800560.
• 28. Vedanayagam Sastriar, http://www.sastriars.org (access on February 2011).

Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).