  Echo and noise suppression is an integral part of a full-duplex communication
system. Many recent acoustic echo cancellation (AEC) systems rely on a separate
adaptive filtering module for linear echo suppression and a neural module for
residual echo suppression. However, not only do adaptive filtering modules
require convergence and remain susceptible to changes in acoustic environments,
but this two-stage framework also often introduces unnecessary delays to the
AEC system when neural modules are already capable of both linear and nonlinear
echo suppression. In this paper, we exploit the offset-compensating ability of
complex time-frequency masks and propose an end-to-end complex-valued neural
network architecture. The building block of the proposed model is a
pseudocomplex extension based on the densely-connected multidilated DenseNet
(D3Net) building block, resulting in a very small network of only 354K
parameters. The architecture utilized the multi-resolution nature of the D3Net
building blocks to eliminate the need for pooling, allowing the network to
extract features using large receptive fields without any loss of output
resolution. We also propose a dual-mask technique for joint echo and noise
suppression with simultaneous speech enhancement. Evaluation on both synthetic
and real test sets demonstrated promising results across multiple energy-based
metrics and perceptual proxies.
