Bayesian unification of sound source localization and separation with permutation resolution

Takuma Otsuka; Katsuhiko Ishiguro; Hiroshi Sawada; Hiroshi G. Okuno

Bayesian unification of sound source localization and separation with permutation resolution

Takuma Otsuka^*, Katsuhiko Ishiguro, Hiroshi Sawada, Hiroshi G. Okuno

^*Corresponding author for this work

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

11 Citations (Scopus)

Abstract

Sound source localization and separation with permutation resolution are essential for achieving a computational auditory scene analysis system that can extract useful information from a mixture of various sounds. Because existing methods cope separately with these problems despite their mutual dependence, the overall result with these approaches can be degraded by any failure in one of these components. This paper presents a unified Bayesian framework to solve these problems simultaneously where localization and separation are regarded as a clustering problem. Experimental results confirm that our method outperforms state-of-the-art methods in terms of the separation quality with various setups including practical reverberant environments.

Original language	English
Title of host publication	Proceedings of the National Conference on Artificial Intelligence
Pages	2038-2045
Number of pages	8
Volume	3
Publication status	Published - 2012
Externally published	Yes
Event	26th AAAI Conference on Artificial Intelligence and the 24th Innovative Applications of Artificial Intelligence Conference, AAAI-12 / IAAI-12 - Toronto, ON Duration: 2012 Jul 22 → 2012 Jul 26

Other

Other	26th AAAI Conference on Artificial Intelligence and the 24th Innovative Applications of Artificial Intelligence Conference, AAAI-12 / IAAI-12
City	Toronto, ON
Period	12/7/22 → 12/7/26

ASJC Scopus subject areas

Software
Artificial Intelligence

Cite this

Otsuka, T, Ishiguro, K, Sawada, H & Okuno, HG 2012, Bayesian unification of sound source localization and separation with permutation resolution. in Proceedings of the National Conference on Artificial Intelligence. vol. 3, pp. 2038-2045, 26th AAAI Conference on Artificial Intelligence and the 24th Innovative Applications of Artificial Intelligence Conference, AAAI-12 / IAAI-12, Toronto, ON, 12/7/22.

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author = "Takuma Otsuka and Katsuhiko Ishiguro and Hiroshi Sawada and Okuno, {Hiroshi G.}",

year = "2012",

language = "English",

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AU - Otsuka, Takuma

AU - Ishiguro, Katsuhiko

AU - Sawada, Hiroshi

AU - Okuno, Hiroshi G.

PY - 2012

Y1 - 2012

N2 - Sound source localization and separation with permutation resolution are essential for achieving a computational auditory scene analysis system that can extract useful information from a mixture of various sounds. Because existing methods cope separately with these problems despite their mutual dependence, the overall result with these approaches can be degraded by any failure in one of these components. This paper presents a unified Bayesian framework to solve these problems simultaneously where localization and separation are regarded as a clustering problem. Experimental results confirm that our method outperforms state-of-the-art methods in terms of the separation quality with various setups including practical reverberant environments.

AB - Sound source localization and separation with permutation resolution are essential for achieving a computational auditory scene analysis system that can extract useful information from a mixture of various sounds. Because existing methods cope separately with these problems despite their mutual dependence, the overall result with these approaches can be degraded by any failure in one of these components. This paper presents a unified Bayesian framework to solve these problems simultaneously where localization and separation are regarded as a clustering problem. Experimental results confirm that our method outperforms state-of-the-art methods in terms of the separation quality with various setups including practical reverberant environments.

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M3 - Conference contribution

AN - SCOPUS:84868297387

SN - 9781577355687

VL - 3

SP - 2038

EP - 2045

BT - Proceedings of the National Conference on Artificial Intelligence

T2 - 26th AAAI Conference on Artificial Intelligence and the 24th Innovative Applications of Artificial Intelligence Conference, AAAI-12 / IAAI-12

Y2 - 22 July 2012 through 26 July 2012

ER -

Bayesian unification of sound source localization and separation with permutation resolution

Abstract

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ASJC Scopus subject areas

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