  This paper applies the dual-signal transformation LSTM network (DTLN) to the
task of real-time acoustic echo cancellation (AEC). The DTLN combines a
short-time Fourier transformation and a learned feature representation in a
stacked network approach, which enables robust information processing in the
time-frequency and in the time domain, which also includes phase information.
The model is only trained on 60~h of real and synthetic echo scenarios. The
training setup includes multi-lingual speech, data augmentation, additional
noise and reverberation to create a model that should generalize well to a
large variety of real-world conditions. The DTLN approach produces
state-of-the-art performance on clean and noisy echo conditions reducing
acoustic echo and additional noise robustly. The method outperforms the
AEC-Challenge baseline by 0.30 in terms of Mean Opinion Score (MOS).
