Bayesian Nonparametrics for Microphone Array Processing

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 unk...

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Bibliographic Details
Published in:IEEE/ACM transactions on audio, speech, and language processing speech, and language processing, 2014-02, Vol.22 (2), p.493-504
Main Authors: Otsuka, Takuma, Ishiguro, Katsuhiko, Sawada, Hiroshi, Okuno, Hiroshi G.
Format: Article
Language:English
Subjects:
Arrays
Audio source separation and enhancement (AUD-SSEN)
Bayes methods
Bayesian nonparametrics
blind source separation
Estimation
microphone array processing
Microphones
sound source localization
Source separation
spatial and multichannel audio (AUD-SMCA)
Time-frequency analysis
time-frequency masking
Vectors
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Summary: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.
ISSN:2329-9290
2329-9304
DOI:10.1109/TASLP.2013.2294582