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ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2025,9,3]]},"abstract":"<jats:p>Recent work has begun to leverage commercial headphones for ear canal health monitoring. However, existing solutions are limited to detecting coarse abnormalities using single-frequency probe tones and specialized hardware. Detecting fine-grained conditions---such as tympanic membrane retraction---remains a significant challenge due to anatomical variability, the limitations of low-sensitivity microphones, and the uncontrolled nature of real-world audio. We present EarCSI, a reconstruction-driven framework that enables precise ear-canal sensing from passive broadband audio using commodity headphones. The core of our system is a lightweight frequency-domain Channel Reconstruction Module, which models the ear canal geometry by analyzing spectral features such as peak spacing and angular propagation behavior. To achieve this, we design a set of novel estimation techniques, including peak-trough-based coarse length inference, spectral angle-based shortest path estimation, and a reflection-aware transfer matrix model that captures cumulative impedance effects. These methods allow the system to reconstruct user-specific ear canal profiles without per-user training or access to invasive scans. Through modeling and experimentation, we uncover a critical constraint: reliable reconstruction requires signal duration, even for short ear canals. To ensure robustness in daily scenarios, we further introduce signal-level and distribution-level interference mitigation strategies that compensate for background noise, headphone misalignment, nonlinearity, and environmental drift. Ultimately, a low-latency classifier extracts health-related features from the reconstructed frequency response and accurately detects tympanic retraction.<\/jats:p>\n          <jats:p>EarCSI achieves over 95% classification accuracy and under 5% reconstruction error across 88 ears using multiple commercial headphones, operating in real-time (168 ms latency). It enables passive and continuous monitoring of tympanic responses during everyday listening, offering a new pathway for personalized auditory health sensing.<\/jats:p>","DOI":"10.1145\/3749516","type":"journal-article","created":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T17:15:45Z","timestamp":1756919745000},"page":"1-48","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Reconstructing Ear Canal Channels for Fine-Grained Detection of Tympanic Membrane Changes"],"prefix":"10.1145","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3728-3502","authenticated-orcid":false,"given":"Yongzhi","family":"Huang","sequence":"first","affiliation":[{"name":"Data Science and Analytics Thrust (DSA), Information Hub (INF), The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-8121-0063","authenticated-orcid":false,"given":"Jiayi","family":"Zhao","sequence":"additional","affiliation":[{"name":"Data Science and Analytics Thrust (DSA), Information Hub (INF), The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2216-0737","authenticated-orcid":false,"given":"Kaishun","family":"Wu","sequence":"additional","affiliation":[{"name":"Data Science and Analytics Thrust (DSA), Information Hub (INF), The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,9,3]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"19","article-title":"Ensembles of deep learning models and transfer learning for ear recognition. 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