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Computational Music Theory and Its Applications to Expressive Performance and Composition

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Abstract

This chapter describes a musical analysis system based on a generative theory of tonal music (GTTM). Music theory provides methodologies for analyzing and transcribing such knowledge, experiences, and skills from a musician’s perspective. Our concern is whether the concepts necessary for musical analysis are sufficiently externalized in musical theory. Given its ability to formalize musical knowledge, GTTM is considered here to be the most promising theory among the many that have been proposed because it captures the aspects of musical phenomena based on the gestalt in the music and follows relatively rigid rules. This chapter also describes music expectation and melody morphing methods that can use the analysis results from the music analysis system. The music expectation method predicts the next notes needed to assist musical novices in playing improvisations. The melody morphing method generates an intermediate melody between two melodies in a systematic order in accordance with a specific numerical measure.

Keywords

  • Preference Rule
  • Music Theory
  • Musical Structure
  • Subsumption Relation
  • Musical Knowledge

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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Notes

  1. 1.

    In this chapter, the word “parameter” is used not only for parameters used to control a system externally but also for internal variables (intermediated variables) that connect submodules.

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Correspondence to Masatoshi Hamanaka .

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Questions

Questions

  1. 1.

    Why are the systems in this chapter potentially so important to automated systems for expressive music performance?

  2. 2.

    What musicological system are ATTA and FATTA designed to automate?

  3. 3.

    What is prolongation reduction?

  4. 4.

    What is a time-span tree?

  5. 5.

    What do parameterization and externalization partially deal with, thus allowing an analysis to be automated on a computer?

  6. 6.

    When is TSRPR5 applied and what does it result in?

  7. 7.

    What are the meet and join operations?

  8. 8.

    How are meet and join used to morph melodies?

  9. 9.

    Give one method for evaluating melody morphing.

  10. 10.

    How could the melody morphing method be used to smoothly change the ShakeGuitar backing in real time from soft backing to heavy soloing?

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Hamanaka, M., Hirata, K., Tojo, S. (2013). Computational Music Theory and Its Applications to Expressive Performance and Composition. In: Kirke, A., Miranda, E. (eds) Guide to Computing for Expressive Music Performance. Springer, London. https://doi.org/10.1007/978-1-4471-4123-5_8

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  • DOI: https://doi.org/10.1007/978-1-4471-4123-5_8

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