  Deep neural network (DNN)-based approaches to acoustic echo cancellation
(AEC) and hybrid speech enhancement systems have gained increasing attention
recently, introducing significant performance improvements to this research
field. Using the fully convolutional recurrent network (FCRN) architecture that
is among state of the art topologies for noise reduction, we present a novel
deep residual echo suppression and noise reduction with up to four input
signals as part of a hybrid speech enhancement system with a linear frequency
domain adaptive Kalman filter AEC. In an extensive ablation study, we reveal
trade-offs with regard to echo suppression, noise reduction, and near-end
speech quality, and provide surprising insights to the choice of the FCRN
inputs: In contrast to often seen input combinations for this task, we propose
not to use the loudspeaker reference signal, but the enhanced signal after AEC,
the microphone signal, and the echo estimate, yielding improvements over
previous approaches by more than 0.2 PESQ points.
