Bayesian unification of sound source localization and separation with permutation resolution

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 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 languageEnglish
Title of host publicationProceedings of the National Conference on Artificial Intelligence
Pages2038-2045
Number of pages8
Volume3
Publication statusPublished - 2012
Externally publishedYes
Event26th AAAI Conference on Artificial Intelligence and the 24th Innovative Applications of Artificial Intelligence Conference, AAAI-12 / IAAI-12 - Toronto, ON
Duration: 2012 Jul 222012 Jul 26

Other

Other26th AAAI Conference on Artificial Intelligence and the 24th Innovative Applications of Artificial Intelligence Conference, AAAI-12 / IAAI-12
CityToronto, ON
Period12/7/2212/7/26

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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  • Cite this

    Otsuka, T., Ishiguro, K., Sawada, H., & Okuno, H. G. (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)