  Our goal is to be able to build a generative model from a deep neural network
architecture to try to create music that has both harmony and melody and is
passable as music composed by humans. Previous work in music generation has
mainly been focused on creating a single melody. More recent work on polyphonic
music modeling, centered around time series probability density estimation, has
met some partial success. In particular, there has been a lot of work based off
of Recurrent Neural Networks combined with Restricted Boltzmann Machines
(RNN-RBM) and other similar recurrent energy based models. Our approach,
however, is to perform end-to-end learning and generation with deep neural nets
alone.
