Microphone Array Beamforming for Speech Enhancement Based on Dynamic Mode Decomposition

Microphone array beamforming is widely used to extract desired speech signals from noisy environments. While most research in this area focuses on utilizing spatial information, less attention is given to the intrinsic physical mechanisms underlying microphone array observations. This paper aims to address this gap by exploring these underlying factors through dynamic mode decomposition (DMD). Our contributions are twofold. 1) We develop a DMD-based signal model for microphone arrays to capture the relationships between observation signals at adjacent microphones. 2) We introduce a DMD-based preprocessing method and a corresponding beamforming approach based on this model. Simulation results show that our proposed method significantly enhances performance compared to conventional beamforming techniques.