Tiny Neural-Network Control of Frequency-Domain Adaptive Filtering for Linear System Identification in Acoustic Echo Cancellation

Acoustic echo cancellation (AEC) in the frequency domain has been a de facto standard in systems for acoustic echo control, but its robustness against double-talk and its agility during rapid echo path changes all the time had to be carefully managed by hand. This paper, therefore, connects theories of model-based and data-driven optimization in order to accomplish a model-based system design with a data-driven replacement of the former hand-tuning. We can demonstrate that the trainable elements of the echo cancellation algorithm may then use very simple architectures with a pronounced minimum of trainable parameters. Experimental results are depicted using the linear subset of the ICASSP-21 AEC challenge data set.