Controlling Speech Distortion in Adaptive Frequency-domain Principal Eigenvector Beamforming

Broadband adaptive beamformers, which use a narrowband SNRmaximization optimization criterion for noise reduction, typically cause distortions of the desired speech signal at the beamformer output. In this paper two methods are investigated to control the speech distortion by comparing the eigenvector beamformer with a maximum likelihood beamformer: One is an analytic solution for the ideal case of absence of reverberation and the other one is a statistically motivated approach. We use the recently introduced gradient-ascent algorithm for adaptive principal eigenvector beamforming and then normalize the filter coefficients by the proposed distortion control methods. Experimental results in terms of the achievable SNR gain and a perceptual speech quality measure are given for the normalized eigenvector beamformer and are compared to standard beamforming methods.