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A Bayesian Approach to Key-Finding

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Music and Artificial Intelligence (ICMAI 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2445))

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Abstract

The key-profile model (originally proposed by Krumhansl and Schmuckler, and modified by Temperley) has proven to be a highly successful approach to key-finding. It appears that the key-profile model can be reinterpreted, with a few small modifications, as a Bayesian probabilistic model. This move sheds interesting light on a number of issues, including the psychological motivation for the key-profile model, other aspects of musical cognition such as metrical analysis, and issues such as ambiguity and expectation.

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References

  1. Bharucha, J. J.: Music Cognition and Perceptual Facilitation: A Connectionist Framework.Music Perception 5 (1987) 1–30

    Google Scholar 

  2. Butler, D.: Describing the Perception of Tonality in Music: A Critique of the Tonal Hierarchy Theory and a Proposal for a Theory of Intervallic Rivalry. Music Perception 6(1989) 219–42

    Google Scholar 

  3. Cemgil, A. T., Desain, P., Kappen, B.: Rhythm Quantization for Transcription. Computer Music Journal 24(2) (2000) 60–76

    Article  Google Scholar 

  4. Cemgil, A. T., Desain, P., Kappen, B, Honing, H.: On Tempo Tracking: Tempogram Representation and Kalman Filtering. Journal of New Music Research 29 (2000) 259–73

    Article  Google Scholar 

  5. Jurafsky, D., Martin, J. H.: Speech and Language Processing. Prentice-Hall, Upper Saddle River, NJ (2000)

    Google Scholar 

  6. Kostka, S.: Instructor’s Manual to Accompany Tonal Harmony. McGraw-Hill, New York(1995)

    Google Scholar 

  7. Kostka, S., Payne, D.: Workbook for Tonal Harmony. McGraw-Hill, New York (1995)

    Google Scholar 

  8. Krumhansl, C.: Cognitive Foundations of Musical Pitch. Oxford University Press, Oxford(1990)

    Google Scholar 

  9. Leman, M.: Music and Schema Theory. Springer-Verlag, Berlin (1995)

    Google Scholar 

  10. Lerdahl, F., Jackendoff, R.: A Generative Theory of Tonal Music. MIT Press, Cambridge MA (1983)

    Google Scholar 

  11. Longuet-Higgins, C., Steedman, M.: On Interpreting Bach. Machine Intelligence 6 (1971) 221–41

    Google Scholar 

  12. Schmuckler, M.: Expectation in Music: Investigation of Melodic and Harmonic Processes.Music Perception 7 (1989) 109–50

    Google Scholar 

  13. Temperley, D.: What’s Key for Key? The Krumhansl-Schmuckler Key-Finding Algorithm Reconsidered. Music Perception 17 (1999) 65–100

    Google Scholar 

  14. Temperley, D.: The Cognition of Basic Musical Structures. MIT Press, Cambridge MA (2001)

    Google Scholar 

  15. Temperley, D., Sleator, D.: Modeling Meter and Harmony: A Preference-Rule Approach. Computer Music Journal 23(1) (1999) 10–27

    Article  Google Scholar 

  16. Vos, P., Van Geenen, E. W.: A Parallel-Processing Key-Finding Model. Music Perception14 (1996) 185–224

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Music Theory, Eastman School of Music, 26 Gibbs St., Rochester, NY

    David Temperley

Authors
  1. David Temperley
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Editor information

Editors and Affiliations

  1. Faculty of Music, University of Edinburgh, 12 Nicolson Square, EH8 9DF, Edinburgh, UK

    Christina Anagnostopoulou  & Miguel Ferrand  & 

  2. Division of Informatics, University of Edinburgh, 80 South Bridge, EH1 1HN, Edinburgh, UK

    Alan Smaill

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© 2002 Springer-Verlag Berlin Heidelberg

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Temperley, D. (2002). A Bayesian Approach to Key-Finding. In: Anagnostopoulou, C., Ferrand, M., Smaill, A. (eds) Music and Artificial Intelligence. ICMAI 2002. Lecture Notes in Computer Science(), vol 2445. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45722-4_18

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  • DOI: https://doi.org/10.1007/3-540-45722-4_18

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44145-8

  • Online ISBN: 978-3-540-45722-0

  • eBook Packages: Springer Book Archive

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