Sleep Sound Event Detection Powered by Learnable Multi-Resolution Adaptive Line Enhancer

Chanwoo Park; Chanwoo Kim

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arXiv · 2610.15532

Sleep Sound Event Detection Powered by Learnable Multi-Resolution Adaptive Line Enhancer

Chanwoo Park·Chanwoo Kim

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Abstract

Sleep-disordered breathing, including snoring, hypopnea, and obstructive apnea, is the hallmark of obstructive sleep apnea, whose severity is quantified by the Apnea-Hypopnea Index (AHI). As conventional polysomnography is costly and clin- ically demanding, microphone-only detection has emerged as a noninvasive alternative for screening. We propose ALE- Confidence Fusion Sound Event Detection (ACF-SED), the first framework that directly injects the per-frame confidence map of an Adaptive Line Enhancer (ALE) into Transformer atten- tion biasing and feature modulation, rather than as mere pre- processing. A Multi-Resolution ALE Bank aggregates three parallel NLMS filters at distinct decorrelation delays, while a Confidence-Guided Cross-Path block couples event and noise streams via gated cross-attention. On the Audio-Polygraphy Dataset for Sleep Apnea Analysis, ACF-SED achieves state- of-the-art detection across Event-F1, Segment-F1, and PSDS, enabling end-to-end AHI estimation.

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Chanwoo Park, Chanwoo Kim. 2026-10-11. Sleep Sound Event Detection Powered by Learnable Multi-Resolution Adaptive Line Enhancer. https://arxiv.org/abs/2610.15532

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