  The quality of speech coded by transform coding is affected by various
artefacts especially when bitrates to quantize the frequency components become
too low. In order to mitigate these coding artefacts and enhance the quality of
coded speech, a post-processor that relies on a-priori information transmitted
from the encoder is traditionally employed at the decoder side. In recent
years, several data-driven post-postprocessors have been proposed which were
shown to outperform traditional approaches. In this paper, we propose PostGAN,
a GAN-based neural post-processor that operates in the sub-band domain and
relies on the U-Net architecture and a learned affine transform. It has been
tested on the recently standardized low-complexity, low-delay bluetooth codec
(LC3) for wideband speech at the lowest bitrate (16 kbit/s). Subjective
evaluations and objective scores show that the newly introduced post-processor
surpasses previously published methods and can improve the quality of coded
speech by around 20 MUSHRA points.
