  We introduce a synergistic approach to double-talk robust acoustic echo
cancellation combining adaptive Kalman filtering with a deep neural
network-based postfilter. The proposed algorithm overcomes the well-known
limitations of Kalman filter-based adaptation control in scenarios
characterized by abrupt echo path changes. As the key innovation, we suggest to
exploit the different statistical properties of the interfering signal
components for robustly estimating the adaptation step size. This is achieved
by leveraging the postfilter near-end estimate and the estimation error of the
Kalman filter. The proposed synergistic scheme allows for rapid reconvergence
of the adaptive filter after abrupt echo path changes without compromising the
steady state performance achieved by state-of-the-art approaches in static
scenarios.
