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Although today's speech communication systems support various bandwidths fromnarrowband to super-wideband and beyond, state-of-the art DNN methods foracoustic echo cancellation (AEC) are lacking modularity and bandwidthscalability. Our proposed DNN model builds upon a fully convolutional recurrentnetwork (FCRN) and introduces scalability over various bandwidths up to afullband (FB) system (48 kHz sampling rate). This modular approach allows jointwideband (WB) pre-training of mask-based AEC and postfilter stages withdedicated losses, followed by a separate training of them on FB data. A thirdlightweight blind bandwidth extension stage is separately trained on FB data,flexibly allowing to extend the WB postfilter output towards higher bandwidthsuntil reaching FB. Thereby, higher frequency noise and echo are reliablysuppressed. On the ICASSP 2022 Acoustic Echo Cancellation Challenge blind testset we report a competitive performance, showing robustness even under highlydelayed echo and dynamic echo path changes.

Bandwidth-Scalable Fully Mask-Based Deep FCRN Acoustic Echo Cancellation and Postfiltering