Modeling inter-node acoustic dependencies with Restricted Boltzmann Machine for distributed microphone array based BSS

An accurate estimation of a source activity information is essential for many speech enhancement algorithms including blind source separation (BSS). In this paper, we propose a novel BSS method that accurately models and estimates the source activity in distributed microphone array (DMA) scenarios. In DMA scenarios, microphones (or in more general term, microphone-nodes) are often spatially distributed to a great degree. If there are multiple source signals in such an environment, the level of each source signal at each microphone-node varies significantly, thus the source activities observable at one microphone-node should be significantly different from those of other nodes. Therefore, it is essential to assume node-specific source activities in DMA scenarios. In the proposed method, the estimation of the node-specific source activities are done by integrating node-wise clustering-based BSS processings based on inter-node acoustic dependencies, i.e., a co-occurrence of the source activities among nodes. To model the co-occurrence relationship, we employ Restricted Boltzmann Machine (RBM) in a similar manner as it is used for collaborative filtering. This paper introduces a probabilistic formulation of the proposed method, and experimentally demonstrates how essential it is to estimate the node-specific source activities for distributed microphone array based BSS.