International Journal of Fuzzy Systems

, Volume 19, Issue 5, pp 1603–1616 | Cite as

NCM-Based Raga Classification Using Musical Features



This paper deals with the study of Carnatic raga identification using musical features. In Carnatic music, there are 72 melakartha ragas. Each raga is denoted by musical notes. The musical features of 72 main ragas are extracted. A number of features such as pitch, timbre, tonal, rhythmic features have been discussed with reference to their ability to distinguish different ragas. Due to the intricate nature of Carnatic music, the concept of neutrosophic logic is used to identify each raga. This is because the concept of neutrosophic logic lies in the neutralities present in between truth and false. This creates a component of indeterminacy, which will make raga identification more accurate and smooth. Neutrosophic Cognitive Maps (NCMs) are drawn based on the musical features and solved. Using neutrosophic logic, a reduced set of musical features is arrived for each raga which can be thought of features characterizing the raga. Each raga is classified using a set of musical features which are solutions of NCMs. This paper represents one of the first attempts to classify all 72 melakartha ragas of using neutrosophic logic.


Carnatic raga MIR matlab toolbox Musical features Neutrosophic Cognitive Maps (NCMs) Neutrosophic logic (NL) 


  1. 1.
    Ashbacher, C.: Introduction to Neutrosophic Logic. American Research Press, Rehoboth (2002)MATHGoogle Scholar
  2. 2.
    Kandasamy, W.B., Smarandache, F.: Fuzzy cognitive maps and Neutrosophic Cognitive Maps. Infinite Study (2003)Google Scholar
  3. 3.
    Vasantha Kandasamy, W.B., Uma, S.: Combined fuzzy cognitive map of socio-economic model. Appl. Sci. Period. 2, 25–27 (2003)Google Scholar
  4. 4.
    Sambamurthy: South Indian Music, p. 4. The Indian Music Publishing House, Madras (1982)Google Scholar
  5. 5.
    Royal Carpet: Glossary of Carnatic Terms (2015). Accessed 20 Nov 2015
  6. 6.
    Sriram, R.S.: Swaras in Carnatic Music (2015). Ipnatlanta Website. Accessed 20 Nov 2015
  7. 7.
    Raman, A., North, P.: The ancient Katapayadi formula and the modern hashing method. IEEE Ann. Hist. Comput. 19(4), 49–52 (1997)MathSciNetCrossRefMATHGoogle Scholar
  8. 8.
    Sridhar, R., Geetha, T.V.: Raga identification of Carnatic music for music information retrieval. Int. J. Recent Trends Eng. 1(1), 1–2 (2009)Google Scholar
  9. 9.
    Sudha, R., Kathirvel, A., Sundaram, R.M.D.: A system of tool for identifying ragas using MIDI. In: Proceedings of Second International Conference on Computer and Electrical Engineering, pp. 644–647 (2009)Google Scholar
  10. 10.
    Prashanth, T.R., Venugopalan, R.: Note identification in Carnatic music from frequency spectrum. In: 2011 International Conference on Communications and Signal Processing (ICCSP). IEEE (2011)Google Scholar
  11. 11.
    Pandey, G., Mishra, C., Ipe, P.: Tansen: a system for automatic raga identification. In: Proceedings of 1st Indian International Conference on Artificial Intelligence, pp. 1–9 (2003)Google Scholar
  12. 12.
    Belle, S., Joshi, R., Rao, P.: Raga identification by using swara intonation. J. ITC Sangeet Res. Acad. 23, 1–3 (2009)Google Scholar
  13. 13.
    Chordia, P., Rae, A.: Raga recognition using pitch-class and pitch-class dyad distributions. In: Proceedings of Austrian Computer Society (OCG), pp. 1–3 (2007)Google Scholar
  14. 14.
    Dighe, P., Karnick, H., Raj, B.: Swara histogram based structural analysis and identification of Indian classical ragas. In: ISMIR, pp. 35–40 (2013)Google Scholar
  15. 15.
    El-Hefenawy, N., Metwally, M.A., Ahmed, Z.M., El-Henawy, I.M.: A review on the applications of neutrosophic sets. J. Comput. Theor. Nanosci. 13(1), 936–944 (2016)CrossRefGoogle Scholar
  16. 16.
    Amin, K.M., Shahin, A.I., Guo, Y.: A novel breast tumor classification algorithm using neutrosophic score features. Measurement 81, 210–220 (2016)CrossRefGoogle Scholar
  17. 17.
    Guo, Y., Şengür, A.: A novel image segmentation algorithm based on neutrosophic similarity clustering. Appl. Soft Comput. 25, 391–398 (2014)CrossRefGoogle Scholar
  18. 18.
    Ma, H., et al.: Toward trustworthy cloud service selection: a time-aware approach using interval neutrosophic set. J. Parallel Distrib. Comput. 96, 75–94 (2016)CrossRefGoogle Scholar
  19. 19.
    Smarandache, F.: Neutrosophy, a new branch of philosophy. Mult.-Valued Logic Int. J. 8(3), 297–384 (2002)MathSciNetMATHGoogle Scholar
  20. 20.
    Smarandache, F.: Neutrosophic set, neutrosophic probability and statistics. In: Proceedings of the First International Conference on Neutrosophy, New Mexico, Published by Xi-quan, Phoenix (2002)Google Scholar
  21. 21.
    Victor Devadoss, A., Aseervatham, S.: A study on the influences of ragas in holy mass songs using Neutrosophic Fuzzy Cognitive Maps (NFCMS). Int. J. Comput. Algorithm 2, 200–206 (2013)Google Scholar
  22. 22.
    Kalaichelvi, A., Gomathy, L.: Application of neutrosophic cognitive maps in the analysis of the problems faced by girl students who got married during the period of study. Int. J. Math. Sci. Appl. 1(3), 1–8 (2011)MathSciNetGoogle Scholar
  23. 23.
    Olivier, L., Toiviainen, P., Eerola, T.: A Matlab Toolbox for Music Information Retrieval. Data Analysis, Machine Learning and Applications, pp. 261–268. Springer, Berlin (2008)Google Scholar
  24. 24.
    Kashef, S., Nezamabadi-pour, H.: An advanced ACO algorithm for feature subset selection. Neurocomputing 147, 271–279 (2015)CrossRefGoogle Scholar
  25. 25.
    Lu, L., Zhang, H.J., Li, S.Z.: Content-based audio classification and segmentation by using support vector machines. Multimed. Syst. 8(6), 482–492 (2003)CrossRefGoogle Scholar

Copyright information

© Taiwan Fuzzy Systems Association and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringPSG College of TechnologyCoimbatoreIndia

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