Learning to Fuse Music Genres with Generative Adversarial Dual Learning

12/05/2017 ∙ by Zhiqian Chen, et al. ∙ 0

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.



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Codes for the paper 'Learning to Fuse Music Genres with Generative Adversarial Dual Learning' ICDM 17

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