11th Speech in Noise Workshop, 10-11 January 2019, Ghent, BE

Performance prediction of the binaural MVDR beamformer with partial noise estimation using a binaural speech intelligibility model

Christopher F. Hauth(a)
Medizinische Physik and Cluster of Excellence Hearing4All, University of Oldenburg, Germany

Nico Gößling, Simon Doclo(b)
University of Oldenburg, Department of Medical Physics and Acoustics and Cluster of Excellence Hearing4All, Oldenburg, Germany

Thomas Brand(b)
Medizinische Physik and Cluster of Excellence Hearing4All, University of Oldenburg, Germany

(a) Presenting
(b) Attending

An objective evaluation of binaural noise reduction algorithms allows for directly comparing the performance of different algorithm realizations. In this study, a binaural speech intelligibility model (BSIM), which mimics the effective binaural processing of human listeners, is used to predict the performance of the binaural minimum-variance distortionless response beamformer with partial noise estimation (BMVDR-N), which aims at preserving the speech component in a reference microphone and a scaled version of the noise component. The BMVDR-N beamformer is evaluated with respect to a predicted change in SRT depending on the parameter η, which controls a trade-off between noise reduction and binaural cue preservation of the noise component. The results show that BSIM benefits from the preserved binaural cues suggesting that the BMVDR-N beamformer can improve the spatial quality of a scene without affecting speech intelligibility.

Last modified 2018-12-08 00:23:30