Initialization-robust Bayesian multipitch analyzer based on psychoacoustical and musical criteria

Daichi Sakaue, Takuma Otsuka, Katsutoshi Itoyama, Hiroshi G. Okuno

研究成果: Conference contribution

抄録

We present a new Bayesian multipitch analyzer that dispenses with a precise optimization of parameter initialization or hyperparameters. Our method uses a new family of prior distribution, characteristic prior; it efficiently restricts the existence region of the latent variables, that is, the product of a conjugate prior and a characteristic function. The update formulas become a simple form that is actually suitable for Gibbs sampling. We construct characteristic priors of harmonic structures based on psychoacoustical and musical knowledge and apply them to nonnegative harmonic factorization. Experimental results improve 5.2 points in F-measure under a tough condition, random initialization with no hyperparameter optimization.

本文言語English
ホスト出版物のタイトルICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ページ226-230
ページ数5
DOI
出版ステータスPublished - 2013 10 18
外部発表はい
イベント2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Vancouver, BC
継続期間: 2013 5 262013 5 31

Other

Other2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
CityVancouver, BC
Period13/5/2613/5/31

ASJC Scopus subject areas

  • Signal Processing
  • Software
  • Electrical and Electronic Engineering

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