  Acoustic echo cancellation (AEC) algorithms have a long-term steady role in
signal processing, with approaches improving the performance of applications
such as automotive hands-free systems, smart home and loudspeaker devices, or
web conference systems. Just recently, very first deep neural network
(DNN)-based approaches were proposed with a DNN for joint AEC and residual echo
suppression (RES)/noise reduction, showing significant improvements in terms of
echo suppression performance. Noise reduction algorithms, on the other hand,
have enjoyed already a lot of attention with regard to DNN approaches, with the
fully convolutional recurrent network (FCRN) architecture being among state of
the art topologies. The recently published impressive echo cancellation
performance of joint AEC/RES DNNs, however, so far came along with an
undeniable impairment of speech quality. In this work we will heal this issue
and significantly improve the near-end speech component quality over existing
approaches. Also, we propose for the first time-to the best of our knowledge-a
pure DNN AEC in the form of an echo estimator, that is based on a competitive
FCRN structure and delivers a quality useful for practical applications.
