Learning to Fuse Music Genres with Generative Adversarial Dual Learning
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.
Keywords
Cite
@article{arxiv.1712.01456,
title = {Learning to Fuse Music Genres with Generative Adversarial Dual Learning},
author = {Zhiqian Chen and Chih-Wei Wu and Yen-Cheng Lu and Alexander Lerch and Chang-Tien Lu},
journal= {arXiv preprint arXiv:1712.01456},
year = {2020}
}
Comments
International Conference on Data Mining - New Orleans, 2017