Advertisement

Automatic Identification of Tala from Tabla Signal

  • Rajib SarkarEmail author
  • Anjishnu Mondal
  • Ankita Singh
  • Sanjoy Kumar Saha
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10730)

Abstract

Tabla is the most common rhythmic instrument in Indian Classical music. A bol the fundamental unit of tabla play and it is produced by striking either or both of the two drums of tabla. Tala (rhythm) is formed with a basic sequence of bols that appears in a cyclic pattern. In this work, bols are automatically segmented from tabla signal following Attack-Decay-Sustain-Release (ADSR) model. Subsequently segmented bols are recognized using low level spectral descriptors and support vector machine (SVM). The identified bol sequence generates transcript of tabla play. A template based matching approach is used to identify tala from the transcript. Proposed system tested successfully with a variety of collection of tabla signal of different talas and it can be utilized in rhythm analysis of music. Moreover, for the learners also the system can help in analyzing their performance.

Keywords

Tabla signal Bol segmentation ADSR model Bol identification Tala transcript Tala identification 

References

  1. 1.
    Bello, J.P., Duxbury, C., Davies, M., Sandler, M.: On the use of phase and energy for musical onset detection in the complex domain. IEEE Signal Process. Lett. 11(6), 553–556 (2004)CrossRefGoogle Scholar
  2. 2.
    Bello, J.P., Daudet, L., Abdallah, S., Duxbury, C., Davies, M., Sandler, M.B.: A tutorial on onset detection in music signals. IEEE Trans. Speech Audio Process. 13(5), 1035–1047 (2005)CrossRefGoogle Scholar
  3. 3.
    Dixon, S.: Onset detection revisited. In: Proceedings of the 9th International Conference on Digital Audio Effects, vol. 120, pp. 133–137 (2006)Google Scholar
  4. 4.
    Grosche, P., Müller, M.: Extracting predominant local pulse information from music recordings. IEEE Trans. Audio, Speech Lang. Process. 19(6), 1688–1701 (2011)CrossRefGoogle Scholar
  5. 5.
    Scheirer, E.D.: Tempo and beat analysis of acoustic musical signals. J. Acoust. Soc. Am. 103(1), 588–601 (1998)CrossRefGoogle Scholar
  6. 6.
    Klapuri, A.: Sound onset detection by applying psychoacoustic knowledge. In: IEEE International Conference of Acoustics, Speech and Signal Processing, Washington, DC, USA, vol. 6, pp. 115–118 (1999)Google Scholar
  7. 7.
    Foote, J.: Visualizing music and audio using self-similarity. In: ACM International Conference on Multimedia (Part 1), MULTIMEDIA 1999, pp. 77–80. ACM, New York (1999)Google Scholar
  8. 8.
    Foote, J.: Automatic audio segmentation using a measure of audio novelty. In: IEEE International Conference on Multimedia and Expo (I), pp. 452–455. IEEE Computer Society (2000)Google Scholar
  9. 9.
    Gillet, O., Richard, G.: Automatic labelling of tabla signals. In: Proceedings of the 4th International Society for Music Information Retrieval Conference (2003)Google Scholar
  10. 10.
    Chordia, P.: Segmentation and recognition of tabla strokes. In: ISMIR, pp. 107–114 (2005)Google Scholar
  11. 11.
    Chordia, P., Rae, A.: Tabla gyan: a system for realtime tabla recognition and resynthesis. In: ICMC (2008)Google Scholar
  12. 12.
    Miron, M.: Automatic detection of hindustani talas. Master’s thesis, Universitat Pompeu Fabra, Barcelona, Spain (2011)Google Scholar
  13. 13.
    Gupta, S., Srinivasamurthy, A., Kumar, M., Murthy, H.A., Serra, X.: Discovery of syllabic percussion patterns in tabla solo recordings. In: International Society for Music Information Retrieval Conference, pp. 385–391 (2015)Google Scholar
  14. 14.
    Sarkar, R., Singh, A., Mondal, A., Saha, S.K.: Automatic extraction and identification of bol from tabla signal. In: ACSS (2017)Google Scholar
  15. 15.
    Fulop, S.A., Fitz, K.: Algorithms for computing the time-corrected instantaneous frequency (reassigned) spectrogram, with applications. J. Acoust. Soc. Am. 119(1), 360–371 (2006)CrossRefGoogle Scholar
  16. 16.
    Zhang, T., Kuo, C.C.J.: Audio content analysis for online audiovisual data segmentation and classification. IEEE Trans. Speech Audio Process. 9(4), 441–457 (2001)CrossRefGoogle Scholar
  17. 17.
    Logan, B., et al.: Mel frequency cepstral coefficients for music modeling. In: ISMIR (2000)Google Scholar
  18. 18.
    Suykens, J.A., Vandewalle, J.: Least squares support vector machine classifiers. Neural Process. Lett. 9(3), 293–300 (1999)CrossRefzbMATHGoogle Scholar
  19. 19.
    Witten, I.H., Frank, E., Hall, M.A., Pal, C.J.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Burlington (2016)Google Scholar
  20. 20.
    Zeng, Z.Q., Yu, H.B., Xu, H.R., Xie, Y.Q., Gao, J.: Fast training support vector machines using parallel sequential minimal optimization. In: 3rd International Conference on Intelligent System and Knowledge Engineering, vol. 1, pp. 997–1001. IEEE (2008)Google Scholar

Copyright information

© Springer-Verlag GmbH Germany 2018

Authors and Affiliations

  1. 1.CSE DepartmentJadavpur UniversityKolkataIndia

Personalised recommendations