M. Blochberger¹, J. Østergaard², R. Ali³, M. Moonen¹, F. Elvander⁴, J. Jensen², T. van Waterschoot¹
¹ KU Leuven
² Aalborg University
³ University of Surrey
⁴ Aalto University
With distributed signal processing gaining traction in the audio and speech processing landscape through the utilization of interconnected devices constituting wireless 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., vanishing 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 pre-trained 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.
- Install Docker
- Clone this repository:
git clone https://github.com/SOUNDS-RESEARCH/asilomar2023-adaptive-coding --recurse-submodules - Build the docker image:
docker build -t asilomar2023sim/simulations . - Run the docker image:
docker run -it --rm -v "$(pwd)/.:/wd" asilomar2023sim/simulations <nr_mc_runs> <random_seed> <number_of_processes> - The raw data results can be found in the
data/directory - The plots can be found in the
plots/directory
This research work was carried out at the ESAT Laboratory of KU Leuven and the Section of AI and Sound of Aalborg University as part of the SOUNDS European Training Network.
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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 956369 |