GNNs have shown promising results in multichannel speech enhancement, where they are used
for extracting clean speech from noisy mixtures captured by multiple microphones [542]. The
authors of a recent study [391] propose a novel approach to multichannel speech enhancement by
combining Graph Convolutional Networks (GCNs) with spatial filtering techniques such as the
Minimum Variance Distortionless Response (MVDR) beamformer. The algorithm aims to extract
speech and noise from noisy signals by computing the Power Spectral Density (PSD) matrices of
the noise and the speech signal of interest and then obtaining optimal weights for the beam former
using a frequency-time mask. The proposed method combines the MVDR beam former with a
super-Gaussian joint maximum a posteriori (SGJMAP) based SE gain function and a GCN-based
separation network. The SGJMAP-based SE gain function is used to enhance the speech signals,
AReviewofDeepLearningTechniquesforSpeechProcessing