A Snoring Sound Dataset for Body Position Recognition: Collection, Annotation, and Analysis

• DOI: 10.48550/arxiv.2307.13346
• Publication Date: 2023-08-20
• Authors: Xiao, Li; Yang, Xiuping; Li, Xinhong; Tu, Weiping; Chen, Xiong; Yi, Weiyan; Lin, Jie; Yang, Yuhong; Ren, Yanzhen
• Keywords: FOS: Computer and information sciences; Sound (cs.SD); Audio and Speech Processing (eess.AS); FOS: Electrical engineering, electronic engineering, information engineering; Computer Science - Sound; Computer Science - Multimedia; Electrical Engineering and Systems Science - Audio and Speech Processing; Multimedia (cs.MM)

Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) is a chronic breathing disorder caused by a blockage in the upper airways. Snoring is a prominent symptom of OSAHS, and previous studies have attempted to identify the obstruction site of the upper airways by snoring sounds. Despite some progress, the classification of the obstruction site remains challenging in real-world clinical settings due to the influence of sleep body position on upper airways. To address this challenge, this paper proposes a snore-based sleep body position recognition dataset (SSBPR) consisting of 7570 snoring recordings, which comprises six distinct labels for sleep body position: supine, supine but left lateral head, supine but right lateral head, left-side lying, right-side lying and prone. Experimental results show that snoring sounds exhibit certain acoustic features that enable their effective utilization for identifying body posture during sleep in real-world scenarios.<br/>Accepted to INTERSPEECH 2023<br/>