Relaxed Binaural LCMV Beamforming
This work addresses binaural hearing aids and audio processing by enabling better control over noise reduction and spatial cue preservation for multiple sound sources, representing an incremental improvement over existing LCMV-based methods.
The paper tackles the problem of binaural beamforming by proposing a relaxed LCMV framework that achieves simultaneous noise reduction and exact binaural cue preservation for the target source, while also preserving binaural cues of multiple interferers to a predefined accuracy, with the method controlling trade-offs per interferer and outperforming BMVDR in preservation accuracy.
In this paper we propose a new binaural beamforming technique which can be seen as a relaxation of the linearly constrained minimum variance (LCMV) framework. The proposed method can achieve simultaneous noise reduction and exact binaural cue preservation of the target source, similar to the binaural minimum variance distortionless response (BMVDR) method. However, unlike BMVDR, the proposed method is also able to preserve the binaural cues of multiple interferers to a certain predefined accuracy. Specifically, it is able to control the trade-off between noise reduction and binaural cue preservation of the interferers by using a separate trade-off parameter per interferer. Moreover, we provide a robust way of selecting these trade-off parameters in such a way that the preservation accuracy for the binaural cues of the interferers is always better than the corresponding ones of the BMVDR. The relaxation of the constraints in the proposed method achieves approximate binaural cue preservation of more interferers than other previously presented LCMV-based binaural beamforming methods that use strict equality constraints.