Microphone Array Post-Filter using Incremental Bayes Learning to Track the Spatial Distributions of Speech and Noise

While current post-filtering algorithms for microphone array applications can enhance beamformer output signals, they assume that the noise is either incoherent or diffuse, and make no allowances for point noise sources which may be strongly correlated across the microphones. In this paper, we present a novel post-filtering algorithm that alleviates this assumption by tracking the spatial as well as spectral distribution of the speech and noise sources present. A generative statistical model is employed to model the speech and noise sources at distinct regions in the soundfield, and incremental Bayesian learning is used to track the model parameters over time. This approach allows a post-filter derived from these parameters to effectively suppress both diffuse ambient noise and interfering point sources. The performance of the proposed approach is evaluated on multiple recordings made in a realistic office environment.