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Flexible and Efficient Bit-Parallel Techniques for Transposition Invariant Approximate Matching in Music Retrieval

  • Kjell Lemström
  • Gonzalo Navarro
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2857)

Abstract

Recent research in music retrieval has shown that a combinatorial approach to the problem could be fruitful. Three distinguishing requirements of this particular problem are (a) approximate searching permitting missing, extra, and distorted notes, (b) transposition invariance, to allow matching a sequence that appears in a different scale, and (c) handling polyphonic music. These combined requirements make up a complex combinatorial problem that is currently under research. On the other hand, bit-parallelism has proved a powerful practical tool for combinatorial pattern matching, both flexible and efficient. In this paper we use bit-parallelism to search for several transpositions at the same time, and obtain speedups of O(w/logk) over the classical algorithms, where the computer word has w bits and k is the error threshold allowed in the match. Although not the best solution for the easier approximation measures, we show that our technique can be adapted to complex cases where no competing method exists, and that are the most interesting in terms of music retrieval.

Keywords

Edit Distance String Match Query Pattern Approximate String Match Computer Word 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Cambouropoulos, E.: A general pitch interval representation: Theory and applications. Journal of New Music Research 25, 231–251 (1996)CrossRefGoogle Scholar
  2. 2.
    Crochemore, M., Iliopoulos, C.S., Pinzon, Y.J., Rytter, W.: Finding motifs with gaps. In: First International Symposium on Music Information Retrieval (ISMIR 2000), Plymouth, MA (2000)Google Scholar
  3. 3.
    Crochemore, M., Iliopoulos, C.S., Navarro, G., Pinzon, Y.: A bit-parallel suffix automaton approach for (δ, γ)-matching in music retrieval. In: Nascimento, M.A., de Moura, E.S., Oliveira, A.L. (eds.) SPIRE 2003. LNCS, vol. 2857, pp. 211–223. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  4. 4.
    Crochemore, M., Iliopoulos, C.S., Pinzon, Y.J., Reid, J.F.: A fast and practical bit-vector algorithm for the longest common subsequence problem. Information Processing Letters 80(6), 279–285 (2001)zbMATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Crochemore, M., Rytter, W.: Text Algorithms. Oxford University Press, Oxford (1994)zbMATHGoogle Scholar
  6. 6.
    Dovey, M.J.: A technique for “regular expression” style searching in polyphonic music. In: the 2nd Annual International Symposium on Music Information Retrieval (ISMIR 2001), Bloomington, IND, October 2001, pp. 179–185 (2001)Google Scholar
  7. 7.
    Holub, J., Iliopoulos, C.S., Mouchard, L.: Distributed string matching using finite automata. Journal of Automata, Languages and Combinatorics 6(2), 191–204 (2001)zbMATHMathSciNetGoogle Scholar
  8. 8.
    Hyyrö, H., Navarro, G.: Faster bit-parallel approximate string matching. In: Apostolico, A., Takeda, M. (eds.) CPM 2002. LNCS, vol. 2373, pp. 203–224. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  9. 9.
    Lemström, K., Laine, P.: Musical information retrieval using musical parameters. In: Proceedings of the 1998 International Computer Music Conference, Ann Arbor, MI, pp. 341–348 (1998)Google Scholar
  10. 10.
    Lemström, K., Tarhio, J.: Transposition invariant pattern matching for multitrack strings. Nordic Journal of Computing (2003) (to appear)Google Scholar
  11. 11.
    Lemström, K., Ukkonen, E.: Including interval encoding into edit distance based music comparison and retrieval. In: Proceedings of the AISB 2000 Symposium on Creative & Cultural Aspects and Applications of AI & Cognitive Science, Birmingham, April 2000, pp. 53–60 (2000)Google Scholar
  12. 12.
    Meredith, D., Lemström, K., Wiggins, G.A.: Algorithms for discovering repeated patterns in multidimensional representations of polyphonic music. Journal of New Music Research 31(4), 321–345 (2002)CrossRefGoogle Scholar
  13. 13.
    MIDI Manufacturers Association, Los Angeles, California. The Complete Detailed MIDI 1.0 Specification (1996)Google Scholar
  14. 14.
    Myers, G.: A fast bit-vector algorithm for approximate string matching based on dynamic programming. Journal of the ACM 46(3), 395–415 (1999). In Farach-Colton, M. (ed.) CPM 1998. LNCS, vol. 1448, Springer, Heidelberg (1998)zbMATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Paul, W., Simon, J.: Decision trees and random access machines. In: Proc. Int’l. Symp. on Logic and Algorithmic, Zurich, pp. 331–340 (1980)Google Scholar
  16. 16.
    Wiggins, G.A., Lemström, K., Meredith, D.: Sia(M): A family of efficient algorithms for translation-invariant pattern matching in multidimensional datasets (submitted)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Kjell Lemström
    • 1
  • Gonzalo Navarro
    • 2
  1. 1.Department of Computer ScienceUniversity of HelsinkiFinland
  2. 2.Department of Computer ScienceUniversity of Chile 

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