Minimum variance distortionless response (MVDR) beamforming is one of the most popular multichannel signal processing techniques for dereverberation and/or noise reduction. However, the MVDR beamformer has the limitation that it must be designed to be dependent on the receiver array geometry. This paper demonstrates an experimental setup and results by designing a deep learning-based MVDR beamformer and applying it to different microphone array configurations. Consequently, it is shown that the deep learning-based MVDR beamformer provides more robust performance under mismatched microphone array configurations than the conventional statistical MVDR one.
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