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

著者情報

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

ジャーナル認証あり

2013 年 E96.A 巻 10 号 p. 1928-1937

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

詳細

抄録

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.

責任著者(Corresponding author)

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