  This paper proposes a new model for music prediction based on Variational
Autoencoders (VAEs). In this work, VAEs are used in a novel way in order to
address two different problems: music representation into the latent space, and
using this representation to make predictions of the future values of the
musical piece. This approach was trained with different songs of a classical
composer. As a result, the system can represent the music in the latent space,
and make accurate predictions. Therefore, the system can be used to compose new
music either from an existing piece or from a random starting point. An
additional feature of this system is that a small dataset was used for
training. However, results show that the system is able to return accurate
representations and predictions in unseen data.
