{"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/a-universal-music-translation-network","title":"A Universal Music Translation Network","arxiv_id":"1805.07848","date":"2018-05-21","proceeding":null,"authors":["Noam Mor","Lior Wolf","Adam Polyak","Yaniv Taigman"],"abstract":"We present a method for translating music across musical instruments, genres,\nand styles. This method is based on a multi-domain wavenet autoencoder, with a\nshared encoder and a disentangled latent space that is trained end-to-end on\nwaveforms. Employing a diverse training dataset and large net capacity, the\ndomain-independent encoder allows us to translate even from musical domains\nthat were not seen during training. The method is unsupervised and does not\nrely on supervision in the form of matched samples between domains or musical\ntranscriptions. We evaluate our method on NSynth, as well as on a dataset\ncollected from professional musicians, and achieve convincing translations,\neven when translating from whistling, potentially enabling the creation of\ninstrumental music by untrained humans.","url_abs":"http://arxiv.org/abs/1805.07848v2","url_pdf":"http://arxiv.org/pdf/1805.07848v2.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":"a-universal-music-translation-network","repo_url":"https://github.com/facebookresearch/music-translation","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"a-universal-music-translation-network","repo_url":"https://github.com/Fengchenghao1996/MI2T_EE380L","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"a-universal-music-translation-network","repo_url":"https://github.com/ShichengChen/WaveNetSeparateAudio","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"a-universal-music-translation-network","repo_url":"https://github.com/scpark20/universal-music-translation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"translation","task_name":"Translation"}],"methods":[{"method_slug":"dilated-causal-convolution","method_name":"Dilated Causal Convolution"},{"method_slug":"mixture-of-logistic-distributions","method_name":"Mixture of Logistic Distributions"},{"method_slug":"wavenet","method_name":"WaveNet"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1805.07848","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}