  FusionGAN is a novel genre fusion framework for music generation that
integrates the strengths of generative adversarial networks and dual learning.
In particular, the proposed method offers a dual learning extension that can
effectively integrate the styles of the given domains. To efficiently quantify
the difference among diverse domains and avoid the vanishing gradient issue,
FusionGAN provides a Wasserstein based metric to approximate the distance
between the target domain and the existing domains. Adopting the Wasserstein
distance, a new domain is created by combining the patterns of the existing
domains using adversarial learning. Experimental results on public music
datasets demonstrated that our approach could effectively merge two genres.
