Dynamic Bayesian networks for symbolic polyhonic pitch modeling

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概要

• 論文の詳細を見る

• The performance of many MIR analysis algorithms, most importantly polyphonic pitch transcription, can be improved by introducing musicological knowledge to the estimation process. We have developed a probabilistically rigorous musicological model that takes into account dependencies between consequent musical notes and consequent chords, as well as the dependencies between chords, notes and the observed note saliences. We investigate its modeling potential by measuring and comparing the cross-entropy with symbolic (MIDI) data.

• 2011-07-20

著者

• Shigeki Sagayama

  東京大学大学院情報理工学系研究科

• Sagayama Shigeki

  東京大学大学院情報理工学系研究科

• Shigeki Sagayama

  The University Of Tokyo

• Emmanuel Vincent

  Institut National de Recherche en Informatique et en Automatique

• Shigeki Sagayama

  Graduate School of Information Science and Technology, The University of Tokyo

関連論文

• Spectrogram consistency and its application to phase reconstruction
• Spectrogram consistency and its application to phase reconstruction
• Dynamic Bayesian networks for symbolic polyhonic pitch modeling
• Input-Output HMM Applied to Automatic Arrangement for Guitars (Preprint)

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