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Error-Tolerant Content-Based Music-Retrieval with Mathematical Morphology

  • Mikko Karvonen
  • Mika Laitinen
  • Kjell Lemström
  • Juho Vikman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6684)

Abstract

In this paper, we show how to apply the framework of mathematical morphology (MM) in order to improve error-tolerance in content-based music retrieval (CBMR) when dealing with approximate retrieval of polyphonic, symbolically encoded music. To this end, we introduce two algorithms based on the MM framework and carry out experiments to compare their performance against well-known algorithms earlier developed for CBMR problems. Although, according to our experiments, the new algorithms do not perform quite as well as the rivaling algorithms in a typical query setting, they provide ease of adjusting the desired error tolerance. Moreover, in certain settings the new algorithms become even faster than their existing counterparts.

Keywords

MIR music information retrieval mathematical morphology geometric music retrieval digital image processing 

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References

  1. 1.
    Barrera Hernández, A.: Finding an o(n2 log n) algorithm is sometimes hard. In: Proceedings of the 8th Canadian Conference on Computational Geometry, pp. 289–294. Carleton University Press, Ottawa (1996)Google Scholar
  2. 2.
    Bloomberg, D., Maragos, P.: Generalized hit-miss operators with applications to document image analysis. In: SPIE Conference on Image Algebra and Morphological Image Processing, pp. 116–128 (1990)Google Scholar
  3. 3.
    Bloomberg, D., Vincent, L.: Pattern matching using the blur hit-miss transform. Journal of Electronic Imaging 9(2), 140–150 (2000)CrossRefGoogle Scholar
  4. 4.
    Clausen, M., Engelbrecht, R., Meyer, D., Schmitz, J.: Proms: A web-based tool for searching in polyphonic music. In: Proceedings of the International Symposium on Music Information Retrieval (ISMIR 2000), Plymouth, MA (October 2000)Google Scholar
  5. 5.
    Clifford, R., Christodoulakis, M., Crawford, T., Meredith, D., Wiggins, G.: A fast, randomised, maximal subset matching algorithm for document-level music retrieval. In: Proceedings of the 7th International Conference on Music Information Retrieval (ISMIR 2006), Victoria, BC, Canada, pp. 150–155 (2006)Google Scholar
  6. 6.
    Heijmans, H.: Mathematical morphology: A modern approach in image processing based on algebra and geometry. SIAM Review 37(1), 1–36 (1995)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Hu, N., Dannenberg, R., Tzanetakis, G.: Polyphonic audio matching and alignment for music retrieval. In: Proc. IEEE WASPAA, pp. 185–188 (2003)Google Scholar
  8. 8.
    Karvonen, M., Lemström, K.: Using mathematical morphology for geometric music information retrieval. In: International Workshop on Machine Learning and Music (MML 2008), Helsinki, Finland (2008)Google Scholar
  9. 9.
    Lemström, K.: Towards more robust geometric content-based music retrieval. In: Proceedings of the 11th International Society for Music Information Retrieval Conference (ISMIR 2010), Utrecht, pp. 577–582 (2010)Google Scholar
  10. 10.
    Lemström, K., Mikkilä, N., Mäkinen, V.: Filtering methods for content-based retrieval on indexed symbolic music databases. Journal of Information Retrieval 13(1), 1–21 (2010)CrossRefGoogle Scholar
  11. 11.
    Lubiw, A., Tanur, L.: Pattern matching in polyphonic music as a weighted geometric translation problem. In: Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR 2004), Barcelona, pp. 289–296 (2004)Google Scholar
  12. 12.
    Romming, C., Selfridge-Field, E.: Algorithms for polyphonic music retrieval: The hausdorff metric and geometric hashing. In: Proceedings of the 8th International Conference on Music Information Retrieval (ISMIR 2007), Vienna, Austria (2007)Google Scholar
  13. 13.
    Typke, R.: Music Retrieval based on Melodic Similarity. Ph.D. thesis, Utrecht University, Netherlands (2007)Google Scholar
  14. 14.
    Ukkonen, E., Lemström, K., Mäkinen, V.: Geometric algorithms for transposition invariant content-based music retrieval. In: Proceedings of the 4th International Conference on Music Information Retrieval (ISMIR 2003), Baltimore, MA, pp. 193–199 (2003)Google Scholar
  15. 15.
    Wiggins, G.A., Lemström, K., Meredith, D.: SIA(M)ESE: An algorithm for transposition invariant, polyphonic content-based music retrieval. In: Proceedings of the 3rd International Conference on Music Information Retrieval (ISMIR 2002), Paris, France, pp. 283–284 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Mikko Karvonen
    • 1
  • Mika Laitinen
    • 1
  • Kjell Lemström
    • 1
  • Juho Vikman
    • 1
  1. 1.Department of Computer ScienceUniversity of HelsinkiFinland

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