Adaptive Coding in Wireless Acoustic Sensor Networks for Distributed Blind System Identification
Matthias Blochberger, Jan Stubbe Østergaard, Randall A. Ali, Marc Moonen, Filip Elvander, Jesper Jensen, Toon van Waterschoot · 2023
With distributed signal processing gaining traction in the audio and speech processing landscape through the utilization of interconnected devices constituting wire-less acoustic sensor networks, additional challenges arise, including optimal data transmission between devices. In this paper, we extend an adaptive distributed blind system identification algorithm by introducing a residual-based adaptive coding scheme to minimize communication costs within the network. We introduce a coding scheme that takes advantage of the convergence of estimates, i.e., van-ishing residuals, to minimize information being sent. The scheme is adaptive, i.e., tracks changes in the estimated system and utilizes entropy coding and adaptive gain to fit the time-varying residual variance to pretrained codebooks. We use a low-complexity approach for gain adaptation, based on a recursive variance estimate. We demonstrate the approach's effectiveness with numerical simulations and its performance in various scenarios.