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

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

doi:10.1109/ICASSP.2013.6637642

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

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

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.

Original language	English
Title of host publication	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Pages	226-230
Number of pages	5
DOIs	https://doi.org/10.1109/ICASSP.2013.6637642
Publication status	Published - 2013 Oct 18
Externally published	Yes
Event	2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Vancouver, BC Duration: 2013 May 26 → 2013 May 31

Other

Other	2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
City	Vancouver, BC
Period	13/5/26 → 13/5/31

Keywords

Bayesian analysis
harmonic clustering
multipitch estimation
nonnegative matrix factorization
overtone corpus

ASJC Scopus subject areas

Signal Processing
Software
Electrical and Electronic Engineering

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10.1109/ICASSP.2013.6637642

Cite this

Initialization-robust Bayesian multipitch analyzer based on psychoacoustical and musical criteria. / Sakaue, Daichi; Otsuka, Takuma; Itoyama, Katsutoshi et al.

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. 2013. p. 226-230 6637642.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Sakaue, D, Otsuka, T, Itoyama, K & Okuno, HG 2013, Initialization-robust Bayesian multipitch analyzer based on psychoacoustical and musical criteria. in ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings., 6637642, pp. 226-230, 2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013, Vancouver, BC, 13/5/26. https://doi.org/10.1109/ICASSP.2013.6637642

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AB - 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.

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

Abstract

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Keywords

ASJC Scopus subject areas

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