A distortionless convolution beamformer design method based on the weighted minimum mean square error for joint dereverberation and denoising

•A distortionless convolution beamformer is designed based on the weighted minimum mean square error for joint dereverberation and denoising.•The proposed distortionless convolution beamformer has a more flexible expression for the optimizations.•The distortion of target signal is effectively reduced through the efficient initialization of the target speech signal and time-varying variance.•The proposed distortionless convolution beamformer can effectively mitigate the residual reverberation and noise by introducing two optimization factors.•Our proposed method has superior performance in joint dereverberation and denoising, as well as exhibits a smaller distortion in the target speech signal. This paper designs a weighted minimum mean square error (WMMSE) based distortionless convolution beamformer (DCBF) for joint dereverberation and denoising. By effectively using WMMSE with the constraint of distortionless, a DCBF is deduced, where the outputs of the weighted prediction error (WPE) filter and the WPE-based minimum variance distortionless response (MVDR) beamformer are combined to initialize target signal for balancing signal distortion, residual reverberation and residual noise. In addition, two optimization factors are introduced to reduce the reverberation and noise when the initialized target signal is used for the solution of beamformer. As a result, the designed beamformer is presented as a linear combination of the WMMSE-based convolution beamformer (CBF) and weighted power minimization distortionless response (WPD) filter. The experimental results demonstrate the superior performance of the designed beamformer for joint dereverberation and denoising compared to the reference methods.