{"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/masking-kernel-for-learning-energy-efficient","title":"Masking Kernel for Learning Energy-Efficient Representations for Speaker Recognition and Mobile Health","arxiv_id":"2302.04161","date":"2023-02-08","proceeding":null,"authors":["Apiwat Ditthapron","Emmanuel O. Agu","Adam C. Lammert"],"abstract":"Modern smartphones possess hardware for audio acquisition and to perform speech processing tasks such as speaker recognition and health assessment. However, energy consumption remains a concern, especially for resource-intensive DNNs. Prior work has improved the DNN energy efficiency by utilizing a compact model or reducing the dimensions of speech features. Both approaches reduced energy consumption during DNN inference but not during speech acquisition. This paper proposes using a masking kernel integrated into gradient descent during DNN training to learn the most energy-efficient speech length and sampling rate for windowing, a common step for sample construction. To determine the most energy-optimal parameters, a masking function with non-zero derivatives was combined with a low-pass filter. The proposed approach minimizes the energy consumption of both data collection and inference by 57%, and is competitive with speaker recognition and traumatic brain injury detection baselines.","url_abs":"https://arxiv.org/abs/2302.04161v2","url_pdf":"https://arxiv.org/pdf/2302.04161v2.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":"masking-kernel-for-learning-energy-efficient","repo_url":"https://github.com/aditthapron/windowmasking","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"speaker-recognition","task_name":"Speaker Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}