Guide to Computing for Expressive Music Performance

pp 205-234


Computational Music Theory and Its Applications to Expressive Performance and Composition

  • Masatoshi HamanakaAffiliated withIntelligent Interaction Technologies, University of Tsukuba Email author 
  • , Keiji HirataAffiliated withFaculty of Systems Information Science, Future University Hakodate
  • , Satoshi TojoAffiliated withSchool of Information Science, Japan Advanced Institute of Science and Technology

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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.