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arXiv · 1712.01456

Learning to Fuse Music Genres with Generative Adversarial Dual Learning

Zhiqian Chen·Chih-Wei Wu·Yen-Cheng Lu·Alexander Lerch·Chang-Tien Lu
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Abstract

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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Zhiqian Chen, Chih-Wei Wu, Yen-Cheng Lu, Alexander Lerch, Chang-Tien Lu. 2017-12-05. Learning to Fuse Music Genres with Generative Adversarial Dual Learning. https://arxiv.org/abs/1712.01456

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