Microphone array post-filter for separation of simultaneous non-stationary sources
This work addresses interference reduction in mobile robotics audio processing, but it is incremental as it builds on existing post-filtering techniques.
The paper tackled the problem of enhancing separated simultaneous non-stationary sources in microphone arrays by proposing a post-filter based on a loudness-domain optimal spectral estimator, which reduces interference with minimal distortion even at low SNR.
Microphone array post-filters have demonstrated their ability to greatly reduce noise at the output of a beamformer. However, current techniques only consider a single source of interest, most of the time assuming stationary background noise. We propose a microphone array post-filter that enhances the signals produced by the separation of simultaneous sources using common source separation algorithms. Our method is based on a loudness-domain optimal spectral estimator and on the assumption that the noise can be described as the sum of a stationary component and of a transient component that is due to leakage between the channels of the initial source separation algorithm. The system is evaluated in the context of mobile robotics and is shown to produce better results than current post-filtering techniques, greatly reducing interference while causing little distortion to the signal of interest, even at very low SNR.