Polyphonic audio-to-score alignment based on Bayesian latent harmonic allocation hidden Markov model

Akira Maezawa*, Hiroshi G. Okuno, Tetsuya Ogata, Masataka Goto

*この研究の対応する著者

研究成果: Conference contribution

12 被引用数 (Scopus)

抄録

This paper presents a Bayesian method for temporally aligning a music score and an audio rendition. A critical problem in audio-to-score alignment is in dealing with the wide variety of timbre and volume of the audio rendition. In contrast with existing works that achieve this through ad-hoc feature design or careful training of tone models, we propose a Bayesian audio-to-score alignment method by modeling music performance as a Bayesian Hidden Markov Model, each state of which emits a Bayesian signal model based on Latent Harmonic Allocation. After attenuating reverberation, variational Bayes method is used to iteratively adapt the alignment, instrument tone model and the volume balance at each position of the score. The method is evaluated using sixty works of classical music of a variety of instrumentation ranging from solo piano to full orchestra. We verify that our method improves the alignment accuracy compared to dynamic time warping based on chroma vector for orchestral music, or our method employed in a maximum likelihood setting.

本文言語English
ホスト出版物のタイトル2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
ページ185-188
ページ数4
DOI
出版ステータスPublished - 2011 8 18
外部発表はい
イベント36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Prague, Czech Republic
継続期間: 2011 5 222011 5 27

出版物シリーズ

名前ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN(印刷版)1520-6149

Conference

Conference36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011
国/地域Czech Republic
CityPrague
Period11/5/2211/5/27

ASJC Scopus subject areas

  • ソフトウェア
  • 信号処理
  • 電子工学および電気工学

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