  Although today's speech communication systems support various bandwidths from
narrowband to super-wideband and beyond, state-of-the art DNN methods for
acoustic echo cancellation (AEC) are lacking modularity and bandwidth
scalability. Our proposed DNN model builds upon a fully convolutional recurrent
network (FCRN) and introduces scalability over various bandwidths up to a
fullband (FB) system (48 kHz sampling rate). This modular approach allows joint
wideband (WB) pre-training of mask-based AEC and postfilter stages with
dedicated losses, followed by a separate training of them on FB data. A third
lightweight blind bandwidth extension stage is separately trained on FB data,
flexibly allowing to extend the WB postfilter output towards higher bandwidths
until reaching FB. Thereby, higher frequency noise and echo are reliably
suppressed. On the ICASSP 2022 Acoustic Echo Cancellation Challenge blind test
set we report a competitive performance, showing robustness even under highly
delayed echo and dynamic echo path changes.
