  Audio codecs are typically transform-domain based and efficiently code
stationary audio signals, but they struggle with speech and signals containing
dense transient events such as applause. Specifically, with these two classes
of signals as examples, we demonstrate a technique for restoring audio from
coding noise based on generative adversarial networks (GAN). A primary
advantage of the proposed GAN-based coded audio enhancer is that the method
operates end-to-end directly on decoded audio samples, eliminating the need to
design any manually-crafted frontend. Furthermore, the enhancement approach
described in this paper can improve the sound quality of low-bit rate coded
audio without any modifications to the existent standard-compliant encoders.
Subjective tests illustrate that the proposed enhancer improves the quality of
speech and difficult to code applause excerpts significantly.
