Learning-Based Multi-Channel Speech Presence Probability Estimation using A Low-Parameter Model and Integration with MVDR Beamforming for Multi-Channel Speech Enhancement

Learning-based a posteriori speech presence probability (SPP) estimation has shown high accuracy in non-stationary noise environments. In this work, we propose using multi-channel information to estimate multi-channel SPP (MC-SPP) based on deep neural networks (DNNs), which contribute to improving multi-channel speech enhancement performance. Firstly, with the observed signal and the MC-SPP as the training data pairs, one low-parameter DNN model is trained to estimate the MC-SPP. Based on the MC-SPP estimate, the noise power density (PSD) and clean speech PSD matrices are updated recursively. With the clean speech PSD matrix, the steering vector is computed using the covariance subtraction method. Subsequently, the minimum variance distortionless response (MVDR) weight is computed with the clean and noise matrices. To further improve multi-channel speech enhancement performance, a new MVDR modification guided by the MC-SPP estimate is proposed. Finally, spatial filtering is performed by integrating the MVDR beamforming. For experiments, we spatially synthesize a real speech dataset in the isotropic noise fields for training and testing. The PESQ, STOI, and DNSMOS scores are used to evaluate speech quality. The experimental results show that, compared with a recently proposed DNN-guided approach, our proposed method provides an effective statistics estimation approach that can further improve multi-channel speech enhancement performance.