Underdetermined source separation with simultaneous DOA estimation without initial value dependency

Tomoya Tachikawa; Kohei Yatabe; Yasuhiro Oikawa

doi:10.1109/IWAENC.2018.8521359

Underdetermined source separation with simultaneous DOA estimation without initial value dependency

Tomoya Tachikawa, Kohei Yatabe, Yasuhiro Oikawa

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

4 Citations (Scopus)

Abstract

In this paper, a sparsity-based method for solving an underde-termind source separation problem is proposed. The proposed method is formulated as a convex optimization problem with two kinds of sparsity priors: sparsity in time-frequency domain and direction-of-arrival (DOA). These priors enable simultaneous estimation of DOA and sound sources, while the estimation result does not depend on an initialization method thanks to the convexity. Experiments using 4 sound sources recorded by 2 microphones confirmed that every random initial value in the proposed method resulted in the same performance which was better than the conventional methods.

Original language	English
Title of host publication	16th International Workshop on Acoustic Signal Enhancement, IWAENC 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	161-165
Number of pages	5
ISBN (Electronic)	9781538681510
DOIs	https://doi.org/10.1109/IWAENC.2018.8521359
Publication status	Published - 2018 Nov 2
Event	16th International Workshop on Acoustic Signal Enhancement, IWAENC 2018 - Tokyo, Japan Duration: 2018 Sept 17 → 2018 Sept 20

Other

Other	16th International Workshop on Acoustic Signal Enhancement, IWAENC 2018
Country/Territory	Japan
City	Tokyo
Period	18/9/17 → 18/9/20

Keywords

1-norm
2-norm
Convex optimization
Disjointness
Group sparsity
L1
L2
Primal-dual splitting

ASJC Scopus subject areas

Signal Processing
Acoustics and Ultrasonics

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10.1109/IWAENC.2018.8521359

Cite this

Underdetermined source separation with simultaneous DOA estimation without initial value dependency. / Tachikawa, Tomoya; Yatabe, Kohei; Oikawa, Yasuhiro.
16th International Workshop on Acoustic Signal Enhancement, IWAENC 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. p. 161-165 8521359.

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

Tachikawa, T, Yatabe, K & Oikawa, Y 2018, Underdetermined source separation with simultaneous DOA estimation without initial value dependency. in 16th International Workshop on Acoustic Signal Enhancement, IWAENC 2018 - Proceedings., 8521359, Institute of Electrical and Electronics Engineers Inc., pp. 161-165, 16th International Workshop on Acoustic Signal Enhancement, IWAENC 2018, Tokyo, Japan, 18/9/17. https://doi.org/10.1109/IWAENC.2018.8521359

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abstract = "In this paper, a sparsity-based method for solving an underde-termind source separation problem is proposed. The proposed method is formulated as a convex optimization problem with two kinds of sparsity priors: sparsity in time-frequency domain and direction-of-arrival (DOA). These priors enable simultaneous estimation of DOA and sound sources, while the estimation result does not depend on an initialization method thanks to the convexity. Experiments using 4 sound sources recorded by 2 microphones confirmed that every random initial value in the proposed method resulted in the same performance which was better than the conventional methods.",

keywords = "1-norm, 2-norm, Convex optimization, Disjointness, Group sparsity, L1, L2, Primal-dual splitting",

author = "Tomoya Tachikawa and Kohei Yatabe and Yasuhiro Oikawa",

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doi = "10.1109/IWAENC.2018.8521359",

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AU - Yatabe, Kohei

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N2 - In this paper, a sparsity-based method for solving an underde-termind source separation problem is proposed. The proposed method is formulated as a convex optimization problem with two kinds of sparsity priors: sparsity in time-frequency domain and direction-of-arrival (DOA). These priors enable simultaneous estimation of DOA and sound sources, while the estimation result does not depend on an initialization method thanks to the convexity. Experiments using 4 sound sources recorded by 2 microphones confirmed that every random initial value in the proposed method resulted in the same performance which was better than the conventional methods.

AB - In this paper, a sparsity-based method for solving an underde-termind source separation problem is proposed. The proposed method is formulated as a convex optimization problem with two kinds of sparsity priors: sparsity in time-frequency domain and direction-of-arrival (DOA). These priors enable simultaneous estimation of DOA and sound sources, while the estimation result does not depend on an initialization method thanks to the convexity. Experiments using 4 sound sources recorded by 2 microphones confirmed that every random initial value in the proposed method resulted in the same performance which was better than the conventional methods.

KW - 1-norm

KW - 2-norm

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KW - Disjointness

KW - Group sparsity

KW - L1

KW - L2

KW - Primal-dual splitting

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BT - 16th International Workshop on Acoustic Signal Enhancement, IWAENC 2018 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

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Underdetermined source separation with simultaneous DOA estimation without initial value dependency

Abstract

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Keywords

ASJC Scopus subject areas

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