  In this paper we propose a lightweight model for frequency bandwidth
extension of speech signals, increasing the sampling frequency from 8kHz to
16kHz while restoring the high frequency content to a level almost
indistinguishable from the 16kHz ground truth. The model architecture is based
on SEANet (Sound EnhAncement Network), a wave-to-wave fully convolutional
model, which uses a combination of feature losses and adversarial losses to
reconstruct an enhanced version of the input speech. In addition, we propose a
variant of SEANet that can be deployed on-device in streaming mode, achieving
an architectural latency of 16ms. When profiled on a single core of a mobile
CPU, processing one 16ms frame takes only 1.5ms. The low latency makes it
viable for bi-directional voice communication systems.
