  We propose a nonlinear acoustic echo cancellation system, which aims to model
the echo path from the far-end signal to the near-end microphone in two parts.
Inspired by the physical behavior of modern hands-free devices, we first
introduce a novel neural network architecture that is specifically designed to
model the nonlinear distortions these devices induce between receiving and
playing the far-end signal. To account for variations between devices, we
construct this network with trainable memory length and nonlinear activation
functions that are not parameterized in advance, but are rather optimized
during the training stage using the training data. Second, the network is
succeeded by a standard adaptive linear filter that constantly tracks the echo
path between the loudspeaker output and the microphone. During training, the
network and filter are jointly optimized to learn the network parameters. This
system requires 17 thousand parameters that consume 500 Million floating-point
operations per second and 40 Kilo-bytes of memory. It also satisfies hands-free
communication timing requirements on a standard neural processor, which renders
it adequate for embedding on hands-free communication devices. Using 280 hours
of real and synthetic data, experiments show advantageous performance compared
to competing methods.
