Bayesian unification of sound source localization and separation with permutation resolution

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

*この研究の対応する著者

研究成果: Conference contribution

11 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルProceedings of the National Conference on Artificial Intelligence
ページ2038-2045
ページ数8
3
出版ステータスPublished - 2012
外部発表はい
イベント26th AAAI Conference on Artificial Intelligence and the 24th Innovative Applications of Artificial Intelligence Conference, AAAI-12 / IAAI-12 - Toronto, ON
継続期間: 2012 7月 222012 7月 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

  • ソフトウェア
  • 人工知能

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