Unified auditory functions based on Bayesian topic model

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

*この研究の対応する著者

研究成果

6 被引用数 (Scopus)

抄録

Existing auditory functions for robots such as sound source localization and separation have been implemented in a cascaded framework whose overall performance may be degraded by any failure in its subsystems. These approaches often require a careful and environment-dependent tuning for each subsystems to achieve better performance. This paper presents a unified framework for sound source localization and separation where the whole system is integrated as a Bayesian topic model. This method improves both localization and separation with a common configuration under various environments by iterative inference using Gibbs sampling. Experimental results from three environments of different reverberation times confirm that our method outperforms state-of-the-art sound source separation methods, especially in the reverberant environments, and shows localization performance comparable to that of the existing robot audition system.

本文言語English
ホスト出版物のタイトルIEEE International Conference on Intelligent Robots and Systems
ページ2370-2376
ページ数7
DOI
出版ステータスPublished - 2012
外部発表はい
イベント25th IEEE/RSJ International Conference on Robotics and Intelligent Systems, IROS 2012 - Vilamoura, Algarve
継続期間: 2012 10 72012 10 12

Other

Other25th IEEE/RSJ International Conference on Robotics and Intelligent Systems, IROS 2012
CityVilamoura, Algarve
Period12/10/712/10/12

ASJC Scopus subject areas

  • 制御およびシステム工学
  • ソフトウェア
  • コンピュータ ビジョンおよびパターン認識
  • コンピュータ サイエンスの応用

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