Bayesian nonparametrics for microphone array processing

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

研究成果: Article査読

38 被引用数 (Scopus)

抄録

Sound source localization and separation from a mixture of sounds are essential functions for computational auditory scene analysis. The main challenges are designing a unified framework for joint optimization and estimating the sound sources under auditory uncertainties such as reverberation or unknown number of sounds. Since sound source localization and separation are mutually dependent, their simultaneous estimation is required for better and more robust performance. A unified model is presented for sound source localization and separation based on Bayesian nonparametrics. Experiments using simulated and recorded audio mixtures show that a method based on this model achieves state-of-the-art sound source separation quality and has more robust performance on the source number estimation under reverberant environments.

本文言語English
ページ(範囲)493-504
ページ数12
ジャーナルIEEE Transactions on Audio, Speech and Language Processing
22
2
DOI
出版ステータスPublished - 2014
外部発表はい

ASJC Scopus subject areas

  • 電子工学および電気工学
  • 音響学および超音波学

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