Adaptive Coding in Wireless Acoustic Sensor Networks for Distributed Blind System Identification

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.