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

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.