Deep Complex-Valued Neural Network-Based Triple-Path Mask and Steering Vector Estimation for Multichannel Target Speech Separation

抄録

We propose a deep complex-valued neural network-based beamforming framework for multichannel target speech separation. The deep complex-valued neural network predicts steering vectors and complex ratio masks for speaker signals. The masked signals are then used to calculate the spatial covariance matrices needed for minimum variance distortionless response (MVDR) beamforming. We propose triple-path modeling for mask estimation, which takes both intrachannel and interchannel features into consideration. Our experimental results revealed that the proposed framework achieves better target speech separation performance than do the baseline methods.