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\(\sigma \)GTTM III: Learning-Based Time-Span Tree Generator Based on PCFG

  • Masatoshi HamanakaEmail author
  • Keiji Hirata
  • Satoshi Tojo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9617)

Abstract

An automatic analyzer based on the generative theory of tonal music (GTTM) for acquiring a time-span tree is described. Although an analyzer based on GTTM was previously reported, it requires manually manipulating 46 adjustable parameters on a computer screen in order to analyze a time-span tree properly. We reformalized the time-span reduction in GTTM on the basis of a probabilistic model called probabilistic context-free grammar, which enables acquiring the most likely time-span tree. Applying leave-one-out cross validation over 300 datasets revealed that the new analyzer outperformed our previously developed GTTM analyzer.

Keywords

Generative theory of tonal music (GTTM) Probabilistic context-free grammar (PCFG) Time-span tree Automatic time-span tree analyzer (ATTA) Full-automatic time-span tree analyzer (FATTA) \(\sigma \)GTTM 

Notes

Acknowledgments

This work was supported in part by JSPS KAKENHI Grant Numbers 23500145, 25330434, and 25700036 and PRESTO, JST.

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Authors and Affiliations

  • Masatoshi Hamanaka
    • 1
    Email author
  • Keiji Hirata
    • 2
  • Satoshi Tojo
    • 3
  1. 1.Kyoto UniversityKyotoJapan
  2. 2.Future University HakodateHakodateJapan
  3. 3.JAIST - Japan Advanced Institute of Science and TechnologyIshikawaJapan

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