Skip to main content

Advertisement

Springer Nature Link
Log in

NCM-Based Raga Classification Using Musical Features

  • Published:
International Journal of Fuzzy Systems Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

References

  1. Ashbacher, C.: Introduction to Neutrosophic Logic. American Research Press, Rehoboth (2002)

    MATH  Google Scholar 

  2. Kandasamy, W.B., Smarandache, F.: Fuzzy cognitive maps and Neutrosophic Cognitive Maps. Infinite Study (2003)

  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. Sambamurthy: South Indian Music, p. 4. The Indian Music Publishing House, Madras (1982)

  5. Royal Carpet: Glossary of Carnatic Terms (2015). http://www.karnatik.com/glosscs.html. Accessed 20 Nov 2015

  6. Sriram, R.S.: Swaras in Carnatic Music (2015). Ipnatlanta Website. www.ipnatlanta.net/camaga/vidyarthi/Carnatic_Basics/Swaras.html. Accessed 20 Nov 2015

  7. Raman, A., North, P.: The ancient Katapayadi formula and the modern hashing method. IEEE Ann. Hist. Comput. 19(4), 49–52 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  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. 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)

  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)

  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)

  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. 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)

  14. Dighe, P., Karnick, H., Raj, B.: Swara histogram based structural analysis and identification of Indian classical ragas. In: ISMIR, pp. 35–40 (2013)

  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)

    Article  Google Scholar 

  16. Amin, K.M., Shahin, A.I., Guo, Y.: A novel breast tumor classification algorithm using neutrosophic score features. Measurement 81, 210–220 (2016)

    Article  Google Scholar 

  17. Guo, Y., Şengür, A.: A novel image segmentation algorithm based on neutrosophic similarity clustering. Appl. Soft Comput. 25, 391–398 (2014)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  19. Smarandache, F.: Neutrosophy, a new branch of philosophy. Mult.-Valued Logic Int. J. 8(3), 297–384 (2002)

    MathSciNet  MATH  Google Scholar 

  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)

  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. 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)

    MathSciNet  Google Scholar 

  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. Kashef, S., Nezamabadi-pour, H.: An advanced ACO algorithm for feature subset selection. Neurocomputing 147, 271–279 (2015)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, PSG College of Technology, Coimbatore, India

    Raghunathan Anitha & Kandasamy Gunavathi

Authors
  1. Raghunathan Anitha
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Kandasamy Gunavathi
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Raghunathan Anitha.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Anitha, R., Gunavathi, K. NCM-Based Raga Classification Using Musical Features. Int. J. Fuzzy Syst. 19, 1603–1616 (2017). https://doi.org/10.1007/s40815-016-0250-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40815-016-0250-5

Keywords