relaxed binaural linearly constrained minimum variance (RJBLCMV) binaural beamformer
This is an implementation of the joint Relaxed binaural linearly constrained minimum variance (RJBLCMV) binaural beamformer using successive convex optimization (SCO) as proposed in
A.I. Koutrouvelis, R.C. Hendriks, R. Heusdens & J. Jensen, "Relaxed binaural LCMV beamforming." IEEE/ACM Trans. on Audio, Speech, and Lang. Proc., 25(1), 137-152, 2017.
In this work, 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.