Location Feature Integration for Clustering-Based Speech Separation in Distributed Microphone Arrays

In distributed microphone arrays (DMAs) the source location information can be defined at the intra and inter-node levels. Indeed, while the first type of information results from the diversity of acoustic channels recorded by microphones embedded in the same node, the second is attributed to the differences between the acoustic channels observed by spatially distributed nodes. Both cues are very useful in DMA processing, and the aim of this paper is to utilize both of them to cluster and separate multiple competing speech signals. To capture the intra-node information, we employ the normalized recording vector, while at the inter-node level, we consider different features including the energy level differences with and without the phase differences between nodes. We model the intra-node information using the Watson mixture model (WMM), and propose using the Gamma mixture model (GaMM), Dirichlet mixture model (DMM), and WMM to model different inter-node location features. Furthermore, we propose several integrations of the intra-node and inter-node feature contributions to cluster speech recordings using the expectation maximization algorithm. Finally, simulation results are provided to demonstrate the performance of all ensuing methods.