A Robust Maximum Likelihood Distortionless Response Beamformer based on a Complex Generalized Gaussian Distribution

TL;DR

This paper introduces a robust multichannel speech enhancement beamformer based on a complex generalized Gaussian distribution, improving robustness and performance over traditional methods.

Contribution

It proposes a novel CGGD-MLDR beamformer that generalizes existing beamformers and enhances robustness in speech enhancement tasks.

Findings

Better PESQ scores in experiments

Reduces to minimum dispersion distortionless response beamformer

Improves robustness in narrowband applications

Abstract

For multichannel speech enhancement, this letter derives a robust maximum likelihood distortionless response beamformer by modeling speech sparse priors with a complex generalized Gaussian distribution, where we refer to as the CGGD-MLDR beamformer. The proposed beamformer can be regarded as a generalization of the minimum power distortionless response beamformer and its improved variations. For narrowband applications, we also reveal that the proposed beamformer reduces to the minimum dispersion distortionless response beamformer, which has been derived with the ${{\ell}_{p}}$-norm minimization. The mechanisms of the proposed beamformer in improving the robustness are clearly pointed out and experimental results show its better performance in PESQ improvement.