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Evolutionary Optimization of Music Performance Annotation

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Computer Music Modeling and Retrieval (CMMR 2004)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3310))

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Abstract

In this paper we present an enhancement of edit distance based music performance annotation. The annotation captures musical expressivity not only in terms of timing deviations but also represents e.g. spontaneous note ornamentation. To reduce the number of errors in automatic performance annotation, some optimization is essential. We have taken an evolutionary approach to optimize the parameter values of cost functions of the edit distance. Automatic optimization is desirable since manual parameter tuning is unfeasible when more than a few performances are taken into account. The validity of the optimized parameter settings is shown by assessing their error-percentage on a test set.

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References

  1. Arcos, J.L., Grachten, M., de Màntaras, R.L.: Extracting performer’s behaviors to annotate cases in a CBR system for musical tempo transformations. In: Ashley, K.D., Bridge, D.G. (eds.) ICCBR 2003. LNCS, vol. 2689, Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  2. Canazza, S., de Poli, G., Rinaldin, S., Vidolin, A.: Sonological analysis of clarinet expressivity. In: Leman, M. (ed.) JIC 1996. LNCS, vol. 1317, pp. 431–440. Springer, Heidelberg (1997)

    Chapter  Google Scholar 

  3. Dannenberg, R.: An on-line algorithm for real-time accompaniment. In: Proceedings of the 1984 International Computer Music Conference. International Computer Music Association (1984)

    Google Scholar 

  4. Desain, P., Honing, H.: Does expressive timing in music performance scale proportionally with tempo? Psychological Research 56, 285–292 (1994)

    Article  Google Scholar 

  5. Gòmez, E., Grachten, M., Amatriain, X., Ll, J.: Arcos Melodic characterization of monophonic recordings for expressive tempo transformations. In: Proceedings of Stockholm Music Acoustics Conference 2003 (2003)

    Google Scholar 

  6. Large, E.W.: Dynamic programming for the analysis of serial behaviors. Behavior Research Methods, Instruments & Computers 25(2), 238–241 (1993)

    Article  Google Scholar 

  7. Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions and reversals. Soviet Physics Doklady 10, 707–710 (1966)

    MathSciNet  Google Scholar 

  8. Mongeau, M., Sankoff, D.: Comparison of musical sequences. Computers and the Humanities 24, 161–175 (1990)

    Article  Google Scholar 

  9. Pardo, B., Birmingham, W.: Improved score following for acoustic performances. In: International Computer Music Conference (ICMC), Goteborg, Sweden (2002), The International Computer Music Association (2002)

    Google Scholar 

  10. Puckette, M., Lippe, A.C.: Score following in practice. In: Proceedings, International Computer Music Conference, pp. 182–185 (1992), International Computer Music Association, San Francisco

    Google Scholar 

  11. Repp, B.H.: Quantitative effects of global tempo on expressive timing in music performance: Some perceptual evidence. Music Perception 13(1), 39–58 (1995)

    Google Scholar 

  12. Smith, L.A., McNab, R.J., Witten, I.H.: Sequence-based melodic comparison: A dynamic programming approach. In: Hewlett, W.B., Selfridge-Field, E. (eds.) Melodic Similarity. Concepts, Procedures, and Applications, Computing in Musicology, pp. 101–118. MIT Press, Cambridge (1998)

    Google Scholar 

  13. Widmer, G.: Large-scale induction of expressive performance rules: First quantitative results. In: Proceedings of the International Computer Music Conference (ICMC 2000), San Francisco, CA, International Computer Music Association

    Google Scholar 

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Author information

Authors and Affiliations

  1. IIIA, Artificial Intelligence Research Institute, CSIC, Spanish Council for Scientific Research, Campus UAB, 08193, Bellaterra, Catalonia, Spain

    Maarten Grachten, Josep Lluís Arcos & Ramon López de Mántaras

Authors
  1. Maarten Grachten
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  2. Josep Lluís Arcos
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  3. Ramon López de Mántaras
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Editor information

Editors and Affiliations

  1. Mærsk Mc-Kinney Møller Institute, University of Southern Denmark, Campus 55, 5230, Odense M, Denmark

    Uffe Kock Wiil

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© 2005 Springer-Verlag Berlin Heidelberg

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Grachten, M., Arcos, J.L., López de Mántaras, R. (2005). Evolutionary Optimization of Music Performance Annotation. In: Wiil, U.K. (eds) Computer Music Modeling and Retrieval. CMMR 2004. Lecture Notes in Computer Science, vol 3310. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31807-1_25

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  • DOI: https://doi.org/10.1007/978-3-540-31807-1_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-24458-5

  • Online ISBN: 978-3-540-31807-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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