Improved tracking performance for distributed node-specific signal enhancement inwireless acoustic sensor networks

A wireless acoustic sensor network is envisaged that is composed of distributed nodes each with several microphones. The goal of each node is to perform signal enhancement, by means of a multi-channel Wiener filter (MWF), in particular to produce an estimate of a desired speech signal. In order to reduce the number of broadcast signals between the nodes, the distributed adaptive node-specific signal estimation (DANSE) algorithm is employed. When each node broadcasts only linearly compressed versions of its microphone signals, the DANSE algorithm still converges as if all uncompressed microphone signals were broadcast. Due to the iterative and statistical nature of the DANSE algorithm several blocks of data are needed before a node can update its node-specific parameters leading to poor tracking performance. In this paper a sub-layer algorithm is presented, that operates under the primary layer DANSE algorithm, which allows nodes to update their parameters during every new block of data and is shown to improve the tracking performance in time-varying environments.