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

For speech enhancement applications, this paper derives a robust maximum likelihood distortionless response (MLDR) beamformer by introducing a complex generalized Gaussian distribution to model speech sparse priors, where we refer to as the CGGD-MLDR beamformer. In addition, a steering vector estimation method is integrated into the iteration frame-work of the proposed CGGD-MLDR beamformer. We show that 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 ℓ
$p$
- norm minimization. The mechanisms of the proposed beamformer in improving the robustness are clearly pointed out and experimental results show its better performance for microphone array speech enhancement in terms of PESQ and ESTOI improvements.