{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/learning-to-fuse-music-genres-with-generative","title":"Learning to Fuse Music Genres with Generative Adversarial Dual Learning","arxiv_id":"1712.01456","date":"2017-12-05","proceeding":null,"authors":["Zhiqian Chen","Chih-Wei Wu","Yen-Cheng Lu","Alexander Lerch","Chang-Tien Lu"],"abstract":"FusionGAN is a novel genre fusion framework for music generation that\nintegrates the strengths of generative adversarial networks and dual learning.\nIn particular, the proposed method offers a dual learning extension that can\neffectively integrate the styles of the given domains. To efficiently quantify\nthe difference among diverse domains and avoid the vanishing gradient issue,\nFusionGAN provides a Wasserstein based metric to approximate the distance\nbetween the target domain and the existing domains. Adopting the Wasserstein\ndistance, a new domain is created by combining the patterns of the existing\ndomains using adversarial learning. Experimental results on public music\ndatasets demonstrated that our approach could effectively merge two genres.","url_abs":"http://arxiv.org/abs/1712.01456v1","url_pdf":"http://arxiv.org/pdf/1712.01456v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"learning-to-fuse-music-genres-with-generative","repo_url":"https://github.com/aquastar/fusion_gan","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"music-generation","task_name":"Music Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}