{"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/towards-on-device-domain-adaptation-for-noise","title":"Towards On-device Domain Adaptation for Noise-Robust Keyword Spotting","arxiv_id":null,"date":"2022-06-13","proceeding":"IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) 2022 6","authors":["Cristian Cioflan","Lukas Cavigelli","Manuele Rusci","Miguel de Prado","Luca Benini"],"abstract":"The accuracy of a keyword spotting model deployed on embedded devices often degrades when the system is exposed to real environments with significant noise. In this paper, we explore a methodology for tailoring a model to on-site noises through on-device domain adaptation, while accounting for the edge computing-associated costs. We show that accuracy improvements by up to 18 % can be obtained by specialising on difficult, previously unseen noise types, on embedded devices with a power budget in the Watt range, with a storage requirement of 1.1 GB. We also demonstrate an accuracy improvement of 1.43% on an ultra-low power platform consuming few-10mW, requiring only 1.47 MB of memory for the adaptation stage, at a one-time energy cost of 5.81 J.","url_abs":"https://ieeexplore.ieee.org/document/9869990","url_pdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9869990","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":"towards-on-device-domain-adaptation-for-noise","repo_url":"https://github.com/pulp-platform/odda-for-kws","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"edge-computing","task_name":"Edge-computing"},{"task_slug":"keyword-spotting","task_name":"Keyword Spotting"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}