Speech enhancement via covariance estimation using Hermitian angle in adaptive beamforming

Conventional adaptive beamformer that employs covariance matrix estimated from received signals is sensitive to array perturbation and signal direction-of-arrival mismatch. To ensure robust performance, only the interference-plus-noise components are used in the computation of covariance matrix. Any leakage of the desired signal will introduce distortion at the beamformer output and under-estimation of the interference and/or noise will lead to a reduction in interference and noise suppression. To identify interference-plus-noise components from the received microphone signals, we analyze the microphone outputs in the short-time Fourier transform domain where we exploit property of the Hermitian angle in complex vector space. This information is then used to determine the interference-plus-noise presence probability which is then incorporated to the updating process of covariance matrix estimation. We show that the proposed algorithm outperforms a recently proposed algorithm for both simulated and recorded data.