Sleep Sound Event Detection Powered by Learnable Multi-Resolution Adaptive Line Enhancer
Chanwoo Park, Chanwoo Kim
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.
Create a lesson
Related papers
LLM-HB: Language-Aware LLM-Guided Hotword Biasing for Code-Switching ASR
Yuxuan He, Genshun Wan, Pengcheng Li et al.
MPASCNet: Magnitude-Phase-Aware Adaptive Spectral Compression for Compressed Music Restoration
Xinlong Deng, Yu Xia, Qiuqiang Kong et al.
Behavior-Conditioned Animal Vocalization Generation: Event-Aware Modeling and Multidimensional Evaluation
Xinlong Deng, Yong Tian, Jian Guan et al.
What Matters in Preference Post-Training of Continuous Autoregressive Speech Language Models
Yingda Shen, Yao Qian, Dekun Chen et al.
The NVVSpeech Challenge 2026: Overview and Lessons for Speech Understanding and Generation with Nonverbal Vocalizations
Liumeng Xue, Yuang Cao, Jingbin Hu et al.
Adaptive Compression and Targeted Perturbation: A Unified Framework for Generalized Audio Deepfake Detection
Jianqiao Cui, Haoxian Zhu, Hanbiao Meng et al.