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Pareto Optimal Binaural MVDR Beamformer with Controllable Interference Suppression

The objective of binaural multi-microphone speech enhancement algorithms can be viewed as a multi-criteria design problem as there are several requirements to be met. When applying distortionless beamforming, it is necessary to suppress interfering sources and ambient background noise, and to extract an undistorted replica of the target source. In the binaural versions, it is also important to preserve the binaural cues of the target and the interference sources. In this paper, we propose a unified Pareto optimization framework for binaural distortionless beamformers, which is achieved by defining a multi-objective problem (MOP) to control the amount of interference suppression and noise reduction simultaneously. The derivation is given for the multi-interference case by introducing separate mean squared error (MSE) cost functions for each of the respective interference sources and the background noise. A Pareto optimal set of solutions is provided for any set of parameters. The performance of the proposed method in a noisy and reverberant environment is presented, demonstrating the impact of the trade-off parameters using real-signal recordings.