  Speech enhancement algorithms based on deep learning have greatly surpassed
their traditional counterparts and are now being considered for the task of
removing acoustic echo from hands-free communication systems. This is a
challenging problem due to both real-world constraints like loudspeaker
non-linearities, and to limited compute capabilities in some communication
systems. In this work, we propose a system combining a traditional acoustic
echo canceller, and a low-complexity joint residual echo and noise suppressor
based on a hybrid signal processing/deep neural network (DSP/DNN) approach. We
show that the proposed system outperforms both traditional and other neural
approaches, while requiring only 5.5% CPU for real-time operation. We further
show that the system can scale to even lower complexity levels.
