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Stream segregation algorithm for pattern matching in polyphonic music databases

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Abstract

As music can be represented symbolically, most of the existing methods extend some string matching algorithms to retrieve musical patterns in a music database. However, not all retrieved patterns are perceptually significant because some of them are, in fact, inaudible. Music is perceived in groupings of musical notes called streams. The process of grouping musical notes into streams is called stream segregation. Stream-crossing musical patterns are perceptually insignificant and should be pruned from the retrieval results. This can be done if all musical notes in a music database are segregated into streams and musical patterns are retrieved from the streams. Findings in auditory psychology are utilized in this paper, in which stream segregation is modelled as a clustering process and an adapted single-link clustering algorithm is proposed. Supported by experiments on real music data, streams are identified by the proposed algorithm with considerable accuracy.

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Notes

  1. In [2], stream segregation is divided into two processes: simultaneous integration and sequential integration. Simultaneous integration is the process of grouping frequencies into musical notes. Sequential integration is the process of grouping musical notes into streams. However, in this paper, stream segregation is referred to the latter process only.

  2. The musical excerpts can be heard at the demonstration web site http://www.cse.cuhk.edu.hk/~wmszeto/stream/index.html.

  3. The self-distance of events is not shown and is marked in “\(-\)” because an event does not form a cluster with itself.

References

  1. Bach JS (1991) Inventions and sinfonias (Two- and three-part inventions), 2nd edition. Alfred Van Nuys, CA

  2. Bregman AS (1990) Auditory scene analysis: the perceptual organization of sound. MIT

  3. Butler D (1992) The musician's guide to perception and cognition. Schirmer Books

  4. Byrd D, Crawford T (2002) Problems of music information retrieval in the real world. Inf Process Manag 38:249–272

    Article  MATH  Google Scholar 

  5. Cambouropoulos E, Crawford T, Iliopoulos C (1999) Pattern processing in melodic sequences: Challenges, caveats and prospects. In Proc. AISB'99 symposium on musical creativity, pp 42–47

  6. Crawford T, Iliopoulos CS, Raman R (1998) String matching techniques for musical similarity and melodic recognition. Comput Musicol 11:73–100

    Google Scholar 

  7. Deutsch D (ed)(1999) The psychology of music, 2nd edition. Academic, San Diego, CA

  8. Gjerdingen RO (1994) Apparent motion in music. Music Percept 11:335–370

    Google Scholar 

  9. Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv 31(3):264–323

    Article  Google Scholar 

  10. Lemstrom K (2000) String matching techniques for music retrieval. PhD thesis, University of Helsinki, Finland

  11. Lemstrom K, Perttu S (2000, October) Semex—an efficient music retrieval prototype. In: First International Symposium on Music Information Retrieval (ISMIR 2000), Plymouth, Massachusetts 23–25

  12. Lemstrom K, Tarhio J (2000, April) Searching monophonic patterns within polyphonic sources. In: Proc. Content-Based Multimedia Information Access (RIAO'2000), volume 2, Paris, France 12–14, pp 1261–1279

  13. Lerdahl F, Jackendoff RS (1983) A generative theory of tonal music. MIT, Cambridge, Mass.

  14. Marsden A (1992) Computer representations and models in music, chapter modelling the perception of musical voices: a case study in rule-based systems. Academic, pp 239–263

  15. MathWorks (2002) Statistics toolbox version 4

  16. McCabe SL, Denham MJ (1997, March) A model of auditory streaming. J Acoust Soc Am 101(3):1611–1621

    Article  Google Scholar 

  17. McNab RJ, Smith LA, Witten IH, Henderson CL (2000) Tune retrieval in the multimedia library. Multimed Tools Appl 10(2/3):113–132

    Article  MATH  Google Scholar 

  18. Melucci M, Orio N (1999) Musical information retrieval using melodic surface. In: Proc. ACM International Conference on Digital Libraries, pp 152–160

  19. Meredith D, Wiggins GA, Lemstrom K (2001, November 21) A geometric approach to repetition discovery and pattern matching in polyphonic music. Presented at Computer Science Colloquium, Department of Computer Science, King's College London

  20. Mutopia Project (2001) Music listing: J. S. Bach's inventions. http://www.mutopiaproject.org/cgibin/make-table.cgi?Composer=BachJS

  21. O'Shaughnessy D (1987) Speech communication: Human and machine. Addison-Wesley, Reading, Mass.

  22. Pickens J (2001) A survey of feature selection techniques for music information retrieval. Technical report, Center for Intelligent Information Retrieval, Deparment of Computer Science, University of Massachussetts

  23. Schachter C (1994) Chopin studies 2, chapter The prelude in E minor Op. 28 No. 4: autograph sources and interpretation. Cambridge University Press, pp 140–161

  24. Temperley D (2001) The cognition of basic musical structures. MIT, Cambridge, Mass.

  25. Theodoridis S, Koutroumbas K (1999) Pattern recognition. Academic, San Diego

  26. Uitdenbgerd A, Zobel J (1999) Melodic matching techniques for large music databases. In: Proc. ACM International Multimedia Conference, pp 57–66

  27. van Noorden LPAS (1975) Temporal coherence in the perception of tone sequences. PhD thesis, Eindhoven University of Technology. Available online at http://alexandria.tue.nl/extra1/PRF2A/7707058.pdf

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Acknowledgments

We are thankful to Dennis Wu and Eos Cheng for their valuable comments.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China

    Wai Man Szeto & Man Hon Wong

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  1. Wai Man Szeto
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  2. Man Hon Wong
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Correspondence to Wai Man Szeto.

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Szeto, W.M., Wong, M.H. Stream segregation algorithm for pattern matching in polyphonic music databases. Multimed Tools Appl 30, 109–127 (2006). https://doi.org/10.1007/s11042-006-0011-9

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