Bayesian nonparametrics for microphone array processing

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

Research output: Contribution to journalArticlepeer-review

38 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)493-504
Number of pages12
JournalIEEE Transactions on Audio, Speech and Language Processing
Issue number2
Publication statusPublished - 2014
Externally publishedYes


  • Audio source separation and enhancement (AUDSSEN)
  • Bayesian nonparametrics
  • Blind source separation
  • Microphone array processing
  • Sound source localization
  • Spatial and multichannel audio (AUD-SMCA)
  • Time-frequency masking

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Acoustics and Ultrasonics


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