Advertisement

Comparing Approaches to the Similarity of Musical Chord Sequences

  • W. B. de Haas
  • Matthias Robine
  • Pierre Hanna
  • Remco C. Veltkamp
  • Frans Wiering
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6684)

Abstract

We present a comparison between two recent approaches to the harmonic similarity of musical chords sequences. In contrast to earlier work that mainly focuses on the similarity of musical notation or musical audio, in this paper we specifically use on the symbolic chord description as the primary musical representation. For an experiment, a large chord sequence corpus was created. In this experiment we compare a geometrical and an alignment approach to harmonic similarity, and measure the effects of chord description detail and a priori key information on retrieval performance. The results show that an alignment approach significantly outperforms a geometrical approach in most cases, but that the geometrical approach is computationally more efficient than the alignment approach. Furthermore, the results demonstrate that a priori key information boosts retrieval performance, and that using a triadic chord representation yields significantly better results than a simpler or more complex chord representation.

Keywords

Music Information Retrieval Musical Harmony Similarity Chord Description Evaluation Ground-truth Data 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Allali, J., Ferraro, P., Hanna, P., Iliopoulos, C.S.: Local transpositions in alignment of polyphonic musical sequences. In: Ziviani, N., Baeza-Yates, R. (eds.) SPIRE 2007. LNCS, vol. 4726, pp. 26–38. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  2. 2.
    Aloupis, G., Fevens, T., Langerman, S., Matsui, T., Mesa, A., Nuñez, Y., Rappaport, D., Toussaint, G.: Algorithms for Computing Geometric Measures of Melodic Similarity. Computer Music Journal 30(3), 67–76 (2004)CrossRefGoogle Scholar
  3. 3.
    Altschul, S., Gish, W., Miller, W., Myers, E., Lipman, D.: Basic Local Alignment Search Tool. Journal of Molecular Biology 215, 403–410 (1990)CrossRefGoogle Scholar
  4. 4.
    Arkin, E., Chew, L., Huttenlocher, D., Kedem, K., Mitchell, J.: An Efficiently Computable Metric for Comparing Polygonal Shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(3), 209–216 (1991)CrossRefzbMATHGoogle Scholar
  5. 5.
    Bello, J., Pickens, J.: A Robust Mid-Level Representation for Harmonic Content in Music Signals. In: Proceedings of the International Symposium on Music Information Retrieval, pp. 304–311 (2005)Google Scholar
  6. 6.
    Cormen, T., Leiserson, C., Rivest, R., Stein, C.: Introduction to Algorithms. MIT Press, Cambridge (2001)zbMATHGoogle Scholar
  7. 7.
    Downie, J.S.: The Music Information Retrieval Evaluation Exchange (2005–2007): A Window into Music Information Retrieval Research. Acoustical Science and Technology 29(4), 247–255 (2008)CrossRefGoogle Scholar
  8. 8.
    Ferraro, P., Hanna, P., Imbert, L., Izard, T.: Accelerating Query-by-Humming on GPU. In: Proceedings of the Tenth International Society for Music Information Retrieval Conference (ISMIR), pp. 279–284 (2009)Google Scholar
  9. 9.
    Gannon, P.: Band-in-a-Box. PG Music (1990), http://www.pgmusic.com/ (last viewed February 2011)
  10. 10.
    de Haas, W.B., Rohrmeier, M., Veltkamp, R.C., Wiering, F.: Modeling Harmonic Similarity Using a Generative Grammar of Tonal Harmony. In: Proceedings of the Tenth International Society for Music Information Retrieval Conference (ISMIR), pp. 549–554 (2009)Google Scholar
  11. 11.
    de Haas, W.B., Veltkamp, R.C., Wiering, F.: Tonal Pitch Step Distance: A Similarity Measure for Chord Progressions. In: Proceedings of the Ninth International Society for Music Information Retrieval Conference (ISMIR), pp. 51–56 (2008)Google Scholar
  12. 12.
    Hanna, P., Robine, M., Rocher, T.: An Alignment Based System for Chord Sequence Retrieval. In: Proceedings of the 2009 Joint International Conference on Digital Libraries, pp. 101–104. ACM, New York (2009)CrossRefGoogle Scholar
  13. 13.
    Hanna, P., Ferraro, P., Robine, M.: On Optimizing the Editing Algorithms for Evaluating Similarity between Monophonic Musical Sequences. Journal of New Music Research 36(4), 267–279 (2007)CrossRefGoogle Scholar
  14. 14.
    Harte, C., Sandler, M., Abdallah, S., Gómez, E.: Symbolic Representation of Musical Chords: A Proposed Syntax for Text Annotations. In: Proceedings of the Sixth International Society for Music Information Retrieval Conference (ISMIR), pp. 66–71 (2005)Google Scholar
  15. 15.
    van Kranenburg, P., Volk, A., Wiering, F., Veltkamp, R.C.: Musical Models for Folk-Song Melody Alignment. In: Proceedings of the Tenth International Society for Music Information Retrieval Conference (ISMIR), pp. 507–512 (2009)Google Scholar
  16. 16.
    Krumhansl, C.: Cognitive Foundations of Musical Pitch. Oxford University Press, USA (2001)CrossRefGoogle Scholar
  17. 17.
    Lerdahl, F.: Tonal Pitch Space. Oxford University Press, Oxford (2001)Google Scholar
  18. 18.
    Manning, C., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, New York (2008)CrossRefzbMATHGoogle Scholar
  19. 19.
    Mauch, M., Dixon, S., Harte, C., Casey, M., Fields, B.: Discovering Chord Idioms through Beatles and Real Book Songs. In: Proceedings of the Eighth International Society for Music Information Retrieval Conference (ISMIR), pp. 255–258 (2007)Google Scholar
  20. 20.
    Mauch, M., Noland, K., Dixon, S.: Using Musical Structure to Enhance Automatic Chord Transcription. In: Proceedings of the Tenth International Society for Music Information Retrieval Conference (ISMIR), pp. 231–236 (2009)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.
    Paiement, J.F., Eck, D., Bengio, S.: A Probabilistic Model for Chord Progressions. In: Proceedings of the Sixth International Conference on Music Information Retrieval (ISMIR), London, UK, pp. 312–319 (2005)Google Scholar
  23. 23.
    Pickens, J., Crawford, T.: Harmonic Models for Polyphonic Music Retrieval. In: Proceedings of the Eleventh International Conference on Information and Knowledge Management, pp. 430–437. ACM, New York (2002)Google Scholar
  24. 24.
    Robine, M., Hanna, P., Ferraro, P.: Music Similarity: Improvements of Edit-based Algorithms by Considering Music Theory. In: Proceedings of the ACM SIGMM International Workshop on Multimedia Information Retrieval (MIR), Augsburg, Germany, pp. 135–141 (2007)Google Scholar
  25. 25.
    Smith, T., Waterman, M.: Identification of Common Molecular Subsequences. Journal of Molecular Biology 147, 195–197 (1981)CrossRefGoogle Scholar
  26. 26.
    Temperley, D.: The Cognition of Basic Musical Structures. MIT Press, Cambridge (2001)Google Scholar
  27. 27.
    Uitdenbogerd, A.L.: Music Information Retrieval Technology. Ph.D. thesis, RMIT University, Melbourne, Australia (July 2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • W. B. de Haas
    • 1
  • Matthias Robine
    • 2
  • Pierre Hanna
    • 2
  • Remco C. Veltkamp
    • 1
  • Frans Wiering
    • 1
  1. 1.Department of Information and Computing SciencesUtrecht UniversityUtrechtThe Netherlands
  2. 2.LaBRI - Université de BordeauxTalence cedexFrance

Personalised recommendations