Multi-Channel Speech Enhancement using a Minimum Variance Distortionless Response Beamformer based on Graph Convolutional Network

Abstract

The Minimum Variance Distortionless Response (MVDR) beamforming algorithm is frequently utilized to extract speech and noise from noisy signals captured from multiple microphones. A frequency-time mask should be employed to compute the Power Spectral Density (PSD) matrices of the noise and the speech signal of interest to obtain the optimal weights for the beamformer. Deep Neural Networks (DNNs) are widely used for estimating time-frequency masks. This paper adopts a novel method using Graph Convolutional Networks (GCNs) to learn spatial correlations among the different channels. GCNs are integrated into the embedding space of a U-Net architecture to estimate a Complex Ideal Ratio Mask (cIRM). We use the cIRM in an MVDR beamformer to further improve the enhancement system. We simulate room acoustics data to experiment extensively with our approach using different types of the microphone array. Results indicate the superiority of our approach when compared to current state-of-the-art methods. The metrics obtained by the proposed method are significantly improved, except the Scale-Invariant Source-to-Distortion Ratio (SI-SDR) score. The Perceptual Evaluation of Speech Quality (PESQ) score shows a noticeable improvement over the baseline models (i.e., 2.207 vs. 2.104 and 2.076). Our implementation of the proposed method can be found in the following link: https://github.com/3i-hust-asr/gnn-mvdr-final.