{"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/singing-voice-separation-with-deep-u-net","title":"Singing Voice Separation with Deep U-Net Convolutional Networks","arxiv_id":null,"date":"2017-10-27","proceeding":"International Society for Music Information Retrieval 2017 10","authors":["Andreas Jansson","Eric Humphrey","Nicola Montecchio","Rachel Bittner","Aparna Kumar","Tillman Weyde"],"abstract":"The decomposition of a music audio signal into its vocal and backing track components is analogous to image-toimage translation, where a mixed spectrogram is transformed into its constituent sources. We propose a novel application of the U-Net architecture — initially developed for medical imaging — for the task of source separation, given its proven capacity for recreating the fine, low-level detail required for high-quality audio reproduction. Through both quantitative evaluation and subjective assessment, experiments demonstrate that the proposed algorithm achieves state-of-the-art performance.","url_abs":"https://www.semanticscholar.org/paper/Singing-Voice-Separation-with-Deep-U-Net-Networks-Jansson-Humphrey/83ea11b45cba0fc7ee5d60f608edae9c1443861d","url_pdf":"https://ejhumphrey.com/assets/pdf/jansson2017singing.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":"singing-voice-separation-with-deep-u-net","repo_url":"https://github.com/tsurumeso/vocal-remover","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"speech-separation","task_name":"Speech Separation"},{"task_slug":"translation","task_name":"Translation"}],"methods":[{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"u-net","method_name":"U-Net"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/speech-separation-on-ikala","task":"Speech Separation","dataset":"iKala","model":"U-Net","rank_in_archive_order":1,"of":1,"metrics":{"NSDR":"11.094 (Vocal); 14.435 (Instrumental)"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}