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An Efficient DTW-Based Approach for Melodic Similarity in Flamenco Singing

  • J. M. Díaz-Báñez
  • J. C. Rizo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8821)

Abstract

We study melodic similarity in flamenco singing by using the Dynamic Time Warping (DTW) distance. Given two melodic contours, the score of the alignment of the two melodies is taken as a similarity measure. Concretely, we consider a particularly representative flamenco repertoire, the tonás, a cappella flamenco singings with free rhythm and high degree of complex ornamentation. We show that the DTW-distance discriminates correctly variations between the styles. In order to speedup the quadratic time and space complexity of the standard DTW, our strategy is to perform an efficient segmentation on the pitch contour before applying dynamic programming. We show that our method achieves better results (both in efficiency and accuracy) than other existing DTW-based similarity measures.

Keywords

Melodic Similarity Alignment Segmentation Flamenco Music 

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References

  1. 1.
    Adams, N., Marquez, D., Wakefield, G.H.: Iterative deepening for melody alignment and retrieval. In: Proc. ISMIR, London (2005)Google Scholar
  2. 2.
    Bartoš, T., Skopal, T.: Revisiting techniques for lowerbounding the dynamic time warping distance. In: Navarro, G., Pestov, V. (eds.) SISAP 2012. LNCS, vol. 7404, pp. 192–208. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  3. 3.
    Cabrera, J.J., Díaz-Báñez, J.M., Escobar, F.J., Gómez, E., Gómez, F., Mora, J.: Comparative Melodic Analysis of A Cappella Flamenco Cantes. In: Conference on Interdisciplinary Musicology, Thessaloniki, Greece (2008)Google Scholar
  4. 4.
    Cornelis, O., Lesaffre, M., Moelants, D., Leman, M.: Access to ethnic music: Advances and perspectives in content-based music information retrieval. Signal Processing 90(4), 1008–1031 (2010)CrossRefzbMATHGoogle Scholar
  5. 5.
    Criel, J., Tsiporkova, E.: Gene Time Expression Warper: a tool for alignment, template matching and visualization of gene expression time series. Bioinformatics 22(2), 251–252 (2006)CrossRefGoogle Scholar
  6. 6.
    Díaz-Báñez, J.M., Mesa, J.A.: Fitting rectilinear polygonal curves to a set of points in the plane. European Journal of Oper. Research 130(1), 214–222 (2001)CrossRefzbMATHGoogle Scholar
  7. 7.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern classification. John Wiley & Sons (2012)Google Scholar
  8. 8.
    Gómez, E., Bonada, J.: Towards Computer-Assisted Flamenco Transcription: An Experimental Comparison of Automatic Transcription Algorithms As Applied to A Cappella Singing. Computer Music Journal 37(2), 73–90 (2013)CrossRefGoogle Scholar
  9. 9.
    Foote, J., Cooper, M.: Visualizing musical structure and rhythm via self-similarity. In: Proceedings of the International Conference on Computer Music, pp. 419–422 (2001)Google Scholar
  10. 10.
    Fu, T.C.: A review on time series data mining. Engineering Applications of Artificial Intelligence 24(1), 164–181 (2011)CrossRefGoogle Scholar
  11. 11.
    Goldstone, R.L., Son, J.Y.: Similarity. Cambridge University Press (2005)Google Scholar
  12. 12.
    Huson, D., Bryant, D.: Application of phylogenetic networks in evolutionary studies. Molecular Biology and Evolution 23, 254–267 (2006)CrossRefGoogle Scholar
  13. 13.
    Itakura, F.: Minimum prediction residual principle applied to speech recognition. IEEE Transactions on Acoustics, Speech and Signal Processing 23(1), 67–72 (1975)CrossRefGoogle Scholar
  14. 14.
    Keogh, E., Pazzani, M.J.: Scaling up dynamic time warping for datamining applications. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 285–289 (2000)Google Scholar
  15. 15.
    Keogh, E., Chu, S., Hart, D., Pazzani, M.: An online algorithm for segmenting time series. In: Proceedings IEEE International Conference on Data Mining (ICDM 2001), pp. 289–296 (2001)Google Scholar
  16. 16.
    Keogh, E., Pazzani, M.: Iterative deepening dynamic time warping for time series. In: Proceedings of the Second SIAM Intl. Conf. on Data Mining (2002)Google Scholar
  17. 17.
    Keogh, E., Ratanamahatana, C.A.: Exact indexing of dynamic time warping. Knowledge and information systems 7(3), 358–386 (2005)CrossRefGoogle Scholar
  18. 18.
    Kroher, N.: Automatic Characterization of Flamenco Singing by Analyzing Audio Recordings. Master thesis, Master Program in Sound and Music Computing, Universitat Pompeu Fabra (2013)Google Scholar
  19. 19.
    Mantel, N., Valand, R.S.: A technique of nonparametric multivariate analysis. Biometrics, 547–558 (1970)Google Scholar
  20. 20.
    Molina, E., Barbancho, I., Gómez, E., Barbancho, A.M., Lorenzo, J.T.: Fundamental Frequency Alignment vs. Note-based Melodic Similarity for Singing Voice Assessment. In: Proceedings of the 8th Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vancouver, Canada (2013)Google Scholar
  21. 21.
    Mongeau, M., Sankoff, D.: Comparison of musical sequences. Computers and the Humanities 24(3), 161–175 (1990)CrossRefGoogle Scholar
  22. 22.
    Mora, J., Gómez, F., Gómez, E., Escobar-Borrego, F., Díaz-Báñez, J.M.: Characterization and melodic similarity of a cappella flamenco cantes. In: Proceedings of ISMIR, Utrecht School of Music, pp. 9–13 (2010)Google Scholar
  23. 23.
    Myers, C., Rabiner, L., Rosenberg, A.: Performance tradeoffs in dynamic time warping algorithms for isolated word recognition. IEEE Transactions on Acoustics, Speech and Signal Processing 28(6), 623–635 (1980)CrossRefzbMATHGoogle Scholar
  24. 24.
    Navarro, J.L., Ropero, M. (eds.): Historia del flamenco. Ed. Tartessos (1995)Google Scholar
  25. 25.
    Olson, D.L., Delen, D.: Advanced data mining techniques. Springer Publishing Company, Incorporated (2008)Google Scholar
  26. 26.
    Pikrakis, A., Theodoridis, S., Kamaroto, D.: Recognition of isolated musical patterns using context dependent dynamic time warping. IEEE Transactions on Speech and Audio Processing, 175–183 (2003)Google Scholar
  27. 27.
    Rabiner, L.R., Juang, B.H.: Fundamentals of speech recognition, vol. 14. PTR Prentice Hall, Englewood Cliffs (1993)Google Scholar
  28. 28.
    Ríos Ruiz, M.: El gran libro del flamenco, editorial Calambur (2002)Google Scholar
  29. 29.
    Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech and Signal Processing 26(1), 43–49 (1978)CrossRefzbMATHGoogle Scholar
  30. 30.
    Salvador, S., Chan, P.: Toward accurate dynamic time warping in linear time and space. Intelligent Data Analysis 11(5), 561–580 (2007)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • J. M. Díaz-Báñez
    • 1
  • J. C. Rizo
    • 1
  1. 1.Universidad de SevillaSevillaSpain

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