{"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/multi-scenario-deep-learning-for-multi","title":"Multi-scenario deep learning for multi-speaker source separation","arxiv_id":"1808.08095","date":"2018-08-24","proceeding":null,"authors":["Jeroen Zegers","Hugo Van hamme"],"abstract":"Research in deep learning for multi-speaker source separation has received a\nboost in the last years. However, most studies are restricted to mixtures of a\nspecific number of speakers, called a specific scenario. While some works\nincluded experiments for different scenarios, research towards combining data\nof different scenarios or creating a single model for multiple scenarios have\nbeen very rare. In this work it is shown that data of a specific scenario is\nrelevant for solving another scenario. Furthermore, it is concluded that a\nsingle model, trained on different scenarios is capable of matching performance\nof scenario specific models.","url_abs":"http://arxiv.org/abs/1808.08095v1","url_pdf":"http://arxiv.org/pdf/1808.08095v1.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":"multi-scenario-deep-learning-for-multi","repo_url":"https://github.com/JeroenZegers/Nabu-MSSS","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"multi-speaker-source-separation","task_name":"Multi-Speaker Source Separation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}