arXiv：cs.LG· Yongzhi Huang, Jiayi Zhao, Kaishun Wu·· 13 小时前
EarLog：将音频播放转为机会式耳部生理传感，实现个性化听力风险感知
EarLog: Opportunistic Ear Physiology Sensing toward Personalized Hearing Risk Awareness
AI 导读
EarLog 将耳机播放声本身变为探测声，通过内容条件化残差传感识别微弱声反射（AR），从而在听歌过程中捕捉耳朵进入保护状态的时刻。该系统以共振约束稀疏 FIR 模型建模短时响应，并结合双麦克风观测、轻度失真恢复和跨会话参考更新应对低 SNR 与环境漂移，可在低成本双麦克风配件上实时运行。
正文
Abstract:Many headphone users risk irreversible hearing damage during high-load listening, yet current protection still relies on sound-pressure limits. Clinical tests detect damage after it becomes measurable, leaving the risk-accumulating process unobserved. The acoustic reflex (AR), an involuntary response to high acoustic load, marks when the ear enters a protective state. AR traces are weak, and existing measurements require dedicated probes and controlled conditions that are difficult to maintain during everyday audio. We present EarLog, which turns playback, the dominant interference, into an opportunistic probe. EarLog introduces content-conditioned residual sensing to suppress variations caused by nonstationary content, individual ear acoustics, and device response. It identifies weak AR through loudness-dependent changes and localized spectral irregularities in the residual. A resonance-constrained sparse finite impulse response (FIR) model captures the short-window response. Dual-microphone observations, mild-distortion recovery, and cross-session reference updates keep detection usable under low signal-to-noise ratio (SNR), fit changes, and environmental drift. EarLog runs in real time on an embedded controller using a low-cost dual-microphone add-on. We evaluate it with 81 participants, 16 headphone models, and more than 400 hours of recordings. EarLog achieves 99.0% sensitivity and 98.7% specificity on a clinically referenced benchmark. EarLog records listener-specific AR events during everyday listening, providing data for future longitudinal studies of hearing risk.
| Comments: | 22 pages. Accepted at ACM MobiCom 2026. Includes appendix |
| Subjects: | Audio and Speech Processing (eess.AS); Machine Learning (cs.LG); Sound (cs.SD); Signal Processing (eess.SP) |
| Cite as: | arXiv:2610.12501 [eess.AS] |
| (or arXiv:2610.12501v1 [eess.AS] for this version) | |
| https://doi.org/10.48550/arXiv.2610.12501 arXiv-issued DOI via DataCite |
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| Related DOI: | https://doi.org/10.1145/3795866.3844456
DOI(s) linking to related resources |
Submission history
From: Yongzhi Huang [view email]
[v1]
Wed, 30 Sep 2026 05:34:16 UTC (17,680 KB)
来源：arXiv：cs.LG · arxiv.org