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New Generation Computing

, Volume 31, Issue 2, pp 89–113 | Cite as

Computational Reconstruction of Cognitive Music Theory

  • Satoshi Tojo
  • Keiji Hirata
  • Masatoshi Hamanaka
Invited Paper

Abstract

In order to obtain a computer-tractable model of music, we first discuss what conditions the music theory should satisfy from the various viewpoints of artificial intelligence and/or other computational notions. Then, we look back on the history of cognitive theory of music, i.e., various attempts to represent our mental understandings and to show music structures. Among which, we especially pay attention to the Generative Theory of Tonal Music (GTTM) by Lehrdahl and Jackendoff, as the most promising candidate of cognitive/computational theory of music. We briefly overview the theory as well as its inherent problems, including the ambiguity of its preference rules. By our recent efforts, we have solved this ambiguity problem by assigning parametrized weights, and thus we could implement an automatic tree analyzer. After we introduce the system architecture, we show our application systems.

Keywords

Music Information Processing Cognitive Theory of Music Computational Musicology Generative Theory of Tonal Music 

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Copyright information

© Ohmsha and Springer Japan 2013

Authors and Affiliations

  • Satoshi Tojo
    • 1
  • Keiji Hirata
    • 2
  • Masatoshi Hamanaka
    • 3
  1. 1.Japan Advanced Institute of Science and TechnologyNomiJapan
  2. 2.Future University HakodateHakodateJapan
  3. 3.University of TsukubaTsukubaJapan

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