Bayesian Nonparametric Approach to Blind Separation of Infinitely Many Sparse Sources
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E96-A No.10 pp.1928-1937
- Publication Date
- 2013/10/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E96.A.1928
- Type of Manuscript
- Special Section PAPER (Special Section on Sparsity-aware Signal Processing)
- Category
Authors
Hirokazu KAMEOKA
The University of Tokyo,NTT Corporation
Misa SATO
The University of Tokyo
Takuma ONO
The University of Tokyo
Nobutaka ONO
National Institute of Informatics
Shigeki SAGAYAMA
The University of Tokyo
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Hirokazu KAMEOKA, Misa SATO, Takuma ONO, Nobutaka ONO, Shigeki SAGAYAMA, "Bayesian Nonparametric Approach to Blind Separation of Infinitely Many Sparse Sources" in IEICE TRANSACTIONS on Fundamentals,
vol. E96-A, no. 10, pp. 1928-1937, October 2013, doi: 10.1587/transfun.E96.A.1928.
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.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E96.A.1928/_p
Copy
@ARTICLE{e96-a_10_1928,
author={Hirokazu KAMEOKA, Misa SATO, Takuma ONO, Nobutaka ONO, Shigeki SAGAYAMA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Bayesian Nonparametric Approach to Blind Separation of Infinitely Many Sparse Sources},
year={2013},
volume={E96-A},
number={10},
pages={1928-1937},
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.},
keywords={},
doi={10.1587/transfun.E96.A.1928},
ISSN={1745-1337},
month={October},}
Copy
TY - JOUR
TI - Bayesian Nonparametric Approach to Blind Separation of Infinitely Many Sparse Sources
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1928
EP - 1937
AU - Hirokazu KAMEOKA
AU - Misa SATO
AU - Takuma ONO
AU - Nobutaka ONO
AU - Shigeki SAGAYAMA
PY - 2013
DO - 10.1587/transfun.E96.A.1928
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E96-A
IS - 10
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - October 2013
AB - 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.
ER -
English
