Bayesian Nonparametric Approach to Blind Separation of Infinitely Many Sparse Sources

Hirokazu KAMEOKA; Misa SATO; Takuma ONO; Nobutaka ONO; Shigeki SAGAYAMA

doi:10.1587/transfun.E96.A.1928

Special Section on Sparsity-aware Signal Processing

Bayesian Nonparametric Approach to Blind Separation of Infinitely Many Sparse Sources

Hirokazu KAMEOKA, Misa SATO, Takuma ONO, Nobutaka ONO, Shigeki SAGAYAMA

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Keywords: underdetermined blind signal separation, speech, sparseness, permutation alignment, Bayesian nonparametrics, direction of arrival, Dirichlet process, stick-breaking construction, variational inference

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2013 Volume E96.A Issue 10 Pages 1928-1937

DOI https://doi.org/10.1587/transfun.E96.A.1928

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Abstract

This paper deals with the problem of underdetermined blind source separation (BSS) where the number of sources is unknown. We propose a BSS approach that simultaneously estimates the number of sources, separates the sources based on the sparseness of speech, estimates the direction of arrival of each source, and performs permutation alignment. We confirmed experimentally that reasonably good separation was obtained with the present method without specifying the number of sources.

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