  We address the challenging open problem of learning an effective latent space
for symbolic music data in generative music modeling. We focus on leveraging
adversarial regularization as a flexible and natural mean to imbue variational
autoencoders with context information concerning music genre and style. Through
the paper, we show how Gaussian mixtures taking into account music metadata
information can be used as an effective prior for the autoencoder latent space,
introducing the first Music Adversarial Autoencoder (MusAE). The empirical
analysis on a large scale benchmark shows that our model has a higher
reconstruction accuracy than state-of-the-art models based on standard
variational autoencoders. It is also able to create realistic interpolations
between two musical sequences, smoothly changing the dynamics of the different
tracks. Experiments show that the model can organise its latent space
accordingly to low-level properties of the musical pieces, as well as to embed
into the latent variables the high-level genre information injected from the
prior distribution to increase its overall performance. This allows us to
perform changes to the generated pieces in a principled way.
