  In recent years, deep neural networks (DNNs) were studied as an alternative
to traditional acoustic echo cancellation (AEC) algorithms. The proposed models
achieved remarkable performance for the separate tasks of AEC and residual echo
suppression (RES). A promising network topology is a fully convolutional
recurrent network (FCRN) structure, which has already proven its performance on
both noise suppression and AEC tasks, individually. However, the combination of
AEC, postfiltering, and noise suppression to a single network typically leads
to a noticeable decline in the quality of the near-end speech component due to
the lack of a separate loss for echo estimation. In this paper, we propose a
two-stage model (Y$^2$-Net) which consists of two FCRNs, each with two inputs
and one output (Y-Net). The first stage (AEC) yields an echo estimate, which -
as a novelty for a DNN AEC model - is further used by the second stage to
perform RES and noise suppression. While the subjective listening test of the
Interspeech 2021 AEC Challenge mostly yielded results close to the baseline,
the proposed method scored an average improvement of 0.46 points over the
baseline on the blind testset in double-talk on the instrumental metric DECMOS,
provided by the challenge organizers.
