  In this paper machine learning networks are explored for their use in
restoring degraded and compressed speech audio. The project intent is to build
a new trained model from voice data to learn features of compression
artifacting distortion introduced by data loss from lossy compression and
resolution loss with an existing algorithm presented in SEGAN: Speech
Enhancement Generative Adversarial Network. The resulting generator from the
model was then to be used to restore degraded speech audio. This paper details
an examination of the subsequent compatibility and operational issues presented
by working with deprecated code, which obstructed the trained model from
successfully being developed. This paper further serves as an examination of
the challenges, limitations, and compatibility in the current state of machine
learning.
