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.

中文翻译：

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学习将音乐流派与生成对抗性双重学习融合

FusionGAN 是一种新颖的音乐生成流派融合框架，它结合了生成对抗网络和双重学习的优势。特别是，所提出的方法提供了一种双重学习扩展，可以有效地整合给定领域的风格。为了有效量化不同域之间的差异并避免梯度消失问题，FusionGAN 提供了基于 Wasserstein 的度量来近似目标域与现有域之间的距离。采用 Wasserstein 距离，通过使用对抗性学习组合现有域的模式来创建新域。公共音乐数据集的实验结果表明，我们的方法可以有效地合并两种流派。