Estimation of MVDR Beamforming Weights Based on Deep Neural Network

摘要

In this paper, we propose a deep learning-based MVDR beamforming weight estimation method. The MVDR beamforming weight can be estimated based on deep learning using GCC-PHAT without the information on the source location, while the MVDR beamforming weight requires information on the source location. As a result of an experiment with REVERB challenge data, the root mean square error between the estimated weight and the MVDR weight was found to be 0.32.