Acoustic Beamforming via Interference-Plus-Noise Covariance Matrix Construction for Interferences and Noise Attenuation

Abstract

The interference-plus-noise covariance matrix (INCM) is essential in improving an acoustic beamformer's interference and noise attenuation performance. In practical implementation, INCM reconstruction is required to remove the signal of interest (SOI) components from the sample covariance matrix. However, some of the interference and noise components are inevitably removed during the INCM reconstruction process to avoid distortion of the desired speech, which deteriorates the interference and noise attenuation performance of the beamformer. This paper proposes constructing an INCM with as much information on the interferences and noise as possible by adding covariance matrices of the spherically diffuse noise, background noise, and interferences. The final INCM is reconstructed by using the principal eigenvector and definition of INCM. The beamformer's weight coefficients are computed by the linearly constrained minimum variance (LCMV) formulation. The proposed method is validated by experiments using a circular microphone array mounted on a tour robot in an exhibition hall. The results show that the proposed beamformer improves the robustness of automatic speech recognition and the performance of robot audition.