  Time-variant factors often occur in real-world full-duplex communication
applications. Some of them are caused by the complex environment such as
non-stationary environmental noises and varying acoustic path while some are
caused by the communication system such as the dynamic delay between the
far-end and near-end signals. Current end-to-end deep neural network (DNN)
based methods usually model the time-variant components implicitly and can
hardly handle the unpredictable time-variance in real-time speech enhancement.
To explicitly capture the time-variant components, we propose a dynamic kernel
generation (DKG) module that can be introduced as a learnable plug-in to a
DNN-based end-to-end pipeline. Specifically, the DKG module generates a
convolutional kernel regarding to each input audio frame, so that the DNN model
is able to dynamically adjust its weights according to the input signal during
inference. Experimental results verify that DKG module improves the performance
of the model under time-variant scenarios, in the joint acoustic echo
cancellation (AEC) and deep noise suppression (DNS) tasks.
