  Generative adversarial networks have been proposed as a way of efficiently
training deep generative neural networks. We propose a generative adversarial
model that works on continuous sequential data, and apply it by training it on
a collection of classical music. We conclude that it generates music that
sounds better and better as the model is trained, report statistics on
generated music, and let the reader judge the quality by downloading the
generated songs.
