  This paper presents an acoustic echo canceler based on a U-Net convolutional
neural network for single-talk and double-talk scenarios. U-Net networks have
previously been used in the audio processing area for source separation
problems because of their ability to reproduce the finest details of audio
signals, but to our knowledge, this is the first time they have been used for
acoustic echo cancellation (AEC). The U-Net hyperparameters have been optimized
to obtain the best AEC performance, but using a reduced number of parameters to
meet a latency restriction of 40 ms. The training and testing of our model have
been carried out within the framework of the 'ICASSP 2021 AEC Challenge'
organized by Microsoft. We have trained the optimized U-Net model with a
synthetic dataset only (S-U-Net) and with a synthetic dataset and the
single-talk set of a real dataset (SR-U-Net), both datasets were released for
the challenge. The S-U-Net model presented better results for double-talk
scenarios, thus their inferred near-end signals from the blind testset were
submitted to the challenge. Our canceler ranked 12th among 17 teams, and 5th
among 10 academia teams, obtaining an overall mean opinion score of 3.57.
