who is snoring? snore based user recognition
Shenghao Li, Jagmohan Chauhan

TL;DR
This paper explores using snore sound analysis with machine learning to identify individual users, aiming to improve passive monitoring for sleep disorders and reduce false alarms in home or clinical settings.
Contribution
It introduces the novel idea that snore sounds contain unique identifiers for user recognition and evaluates machine learning models on an open dataset for this purpose.
Findings
Achieved around 90% accuracy in user identification and verification
Demonstrated the feasibility of snore-based user recognition
Provided a foundation for passive sleep monitoring applications
Abstract
Snoring is one of the most prominent symptoms of Obstructive Sleep Apnea-Hypopnea Syndrome (OSAH), a highly prevalent disease that causes repetitive collapse and cessation of the upper airway. Thus, accurate snore sound monitoring and analysis is crucial. However, the traditional monitoring method polysomnography (PSG) requires the patients to stay at a sleep clinic for the whole night and be connected to many pieces of equipment. An alternative and less invasive way is passive monitoring using a smartphone at home or in the clinical settings. But, there is a challenge: the environment may be shared by people such that the raw audio may contain the snore activities of the bed partner or other person. False capturing of the snoring activity could lead to critical false alarms and misdiagnosis of the patients. To address this limitation, we propose a hypothesis that snore sound contains…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsObstructive Sleep Apnea Research · Tracheal and airway disorders
