  Multi-channel speech enhancement aims to extract clean speech from a noisy
mixture using signals captured from multiple microphones. Recently proposed
methods tackle this problem by incorporating deep neural network models with
spatial filtering techniques such as the minimum variance distortionless
response (MVDR) beamformer. In this paper, we introduce a different research
direction by viewing each audio channel as a node lying in a non-Euclidean
space and, specifically, a graph. This formulation allows us to apply graph
neural networks (GNN) to find spatial correlations among the different channels
(nodes). We utilize graph convolution networks (GCN) by incorporating them in
the embedding space of a U-Net architecture. We use LibriSpeech dataset and
simulate room acoustics data to extensively experiment with our approach using
different array types, and number of microphones. Results indicate the
superiority of our approach when compared to prior state-of-the-art method.
