A generalized binaural MVDR beamformer with interferer relative transfer function preservation

In addition to interference and noise reduction, an important objective of binaural speech enhancement algorithms is the preservation of the binaural cues of both the target and the undesired sound sources. For directional sources, this can be achieved by preserving the relative transfer function (RTF). The recently proposed binaural minimum variance distortionless response (BMVDR) beamformer preserves the RTF of the target, but typically distorts the RTF of the interfering sources. Recently, two extensions of the BMVDR beamformer were proposed preserving the RTFs of both the target and the interferer, namely, the binaural linearly constrained minimum variance (BLCMV) and the BMVDR-RTF beamformers. In this paper, we generalize the BMVDR-RTF to trade off interference reduction and noise reduction. Three special cases of the proposed beamformer are examined, either maximizing the signal-to-interference-and-noise ratio (SINR), the signal-to-noise ratio (SNR), or the signal-tointerference ratio (SIR). Experimental validations in an office environment validate our theoretical results.