  Lossy audio codecs compress (and decompress) digital audio streams by
removing information that tends to be inaudible in human perception. Under high
compression rates, such codecs may introduce a variety of impairments in the
audio signal. Many works have tackled the problem of audio enhancement and
compression artifact removal using deep learning techniques. However, only a
few works tackle the restoration of heavily compressed audio signals in the
musical domain. In such a scenario, there is no unique solution for the
restoration of the original signal. Therefore, in this study, we test a
stochastic generator of a Generative Adversarial Network (GAN) architecture for
this task. Such a stochastic generator, conditioned on highly compressed
musical audio signals, could one day generate outputs indistinguishable from
high-quality releases. Therefore, the present study may yield insights into
more efficient musical data storage and transmission. We train stochastic and
deterministic generators on MP3-compressed audio signals with 16, 32, and 64
kbit/s. We perform an extensive evaluation of the different experiments
utilizing objective metrics and listening tests. We find that the models can
improve the quality of the audio signals over the MP3 versions for 16 and 32
kbit/s and that the stochastic generators are capable of generating outputs
that are closer to the original signals than those of the deterministic
generators.
