{"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/faster-iva-update-rules-for-independent","title":"Faster IVA: Update Rules for Independent Vector Analysis based on Negentropy and the Majorize-Minimize Principle","arxiv_id":"2003.09531","date":"2020-03-20","proceeding":null,"authors":["Andreas Brendel","Walter Kellermann"],"abstract":"Algorithms for Blind Source Separation (BSS) of acoustic signals require efficient and fast converging optimization strategies to adapt to nonstationary signal statistics and time-varying acoustic scenarios. In this paper, we derive fast converging update rules from a negentropy perspective, which are based on the Majorize-Minimize (MM) principle and eigenvalue decomposition. The presented update rules are shown to outperform competing state-of-the-art methods in terms of convergence speed at a comparable runtime due to the restriction to unitary demixing matrices. This is demonstrated by experiments with recorded real-world data.","url_abs":"https://arxiv.org/abs/2003.09531v2","url_pdf":"https://arxiv.org/pdf/2003.09531v2.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":"faster-iva-update-rules-for-independent","repo_url":"https://github.com/tky823/ssspy","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"blind-source-separation","task_name":"blind 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}