  Over the past several years, deep learning for sequence modeling has grown in
popularity. To achieve this goal, LSTM network structures have proven to be
very useful for making predictions for the next output in a series. For
instance, a smartphone predicting the next word of a text message could use an
LSTM. We sought to demonstrate an approach of music generation using Recurrent
Neural Networks (RNN). More specifically, a Long Short-Term Memory (LSTM)
neural network. Generating music is a notoriously complicated task, whether
handmade or generated, as there are a myriad of components involved. Taking
this into account, we provide a brief synopsis of the intuition, theory, and
application of LSTMs in music generation, develop and present the network we
found to best achieve this goal, identify and address issues and challenges
faced, and include potential future improvements for our network.
