  In this paper, we propose a residual echo suppression method using a UNet
neural network that directly maps the outputs of a linear acoustic echo
canceler to the desired signal in the spectral domain. This system embeds a
design parameter that allows a tunable tradeoff between the desired-signal
distortion and residual echo suppression in double-talk scenarios. The system
employs 136 thousand parameters, and requires 1.6 Giga floating-point
operations per second and 10 Mega-bytes of memory. The implementation satisfies
both the timing requirements of the AEC challenge and the computational and
memory limitations of on-device applications. Experiments are conducted with
161~h of data from the AEC challenge database and from real independent
recordings. We demonstrate the performance of the proposed system in real-life
conditions and compare it with two competing methods regarding echo suppression
and desired-signal distortion, generalization to various environments, and
robustness to high echo levels.
