Title: Symbolic Music Genre Transfer with CycleGAN,
Abstract: Deep generative models such as Variational Autoencoders (VAEs) and Generative
Adversarial Networks (GANs) have recently been applied to style and domain
transfer for images, and in the case of VAEs, music. GAN-based models employing
several generators and some form of cycle consistency loss have been among the
most successful for image domain transfer. In this paper we apply such a model
to symbolic music and show the feasibility of our approach for music genre
transfer. Evaluations using separate genre classifiers show that the style
transfer works well. In order to improve the fidelity of the transformed music,
we add additional discriminators that cause the generators to keep the
structure of the original music mostly intact, while still achieving strong
genre transfer. Visual and audible results further show the potential of our
approach. To the best of our knowledge, this paper represents the first
application of GANs to symbolic music domain transfer.