2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDSPE-18.3
Paper Title CUE-PRESERVING MMSE FILTER WITH BAYESIAN SNR MARGINALIZATION FOR BINAURAL SPEECH ENHANCEMENT
Authors Stefan Thaleiser, Gerald Enzner, Ruhr-Universität Bochum, Germany
SessionSPE-18: Speech Enhancement 4: Multi-channel Processing
LocationGather.Town
Session Time:Wednesday, 09 June, 14:00 - 14:45
Presentation Time:Wednesday, 09 June, 14:00 - 14:45
Presentation Poster
Topic Speech Processing: [SPE-ENHA] Speech Enhancement and Separation
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Binaural speech enhancement has often suffered from the trade-off between noise reduction and spatial cue preservation. The common-gain filtering of noisy speech under minimum mean-square error (MMSE) turned out as a viable approach, which resembles the format of Wiener-filtering spectral enhancement. Those techniques critically require the estimation of the local time-varying a-priori SNR. In single-channel approaches, it has been recently shown that local a-priori SNR can be marginalized in a Bayesian sense with an MMSE approach. In this paper, we translate the single-channel approach into a binaural Bayesian SNR marginalization, based on a binaural a-priori SNR definition and a related hyperprior. The overall MMSE solution then turns into a posterior expectation of an informed cue-preserving Wiener filter function, the computation of which is governed by binaural a-posteriori SNR and global SNR (i.e., the hyperprior mean). The resulting MMSE solution is thus easy to implement and performance consistently stands at the top of our evaluation by segmental SNR, PESQ, and STOI computational metrics.