Abstract: Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) is a chronic respiratory disorder caused by the obstruction of the upper airway. The treatment approach for OSAHS varies based on the individual patient’s breathing route and phase during snoring. Extensive research has been conducted to identify various snoring patterns, including the breathing route and the breathing phase during snoring. However, the identification of breathing routes and phases in snoring sounds is still in the early stages due to the limited availability of comprehensive datasets with scientifically annotated nocturnal snoring sounds. To address this challenge, this study presents ONEI, an innovative dataset designed for recognizing and analyzing snoring patterns. ONEI encompasses 5171 snoring recordings and is annotated with four distinct labels, namely nasal-dominant inspiratory snoring, nasal-dominant expiratory snoring, oral inspiratory snoring, and oral expiratory snoring. Experimental evaluations reveal discernible acoustic features in snoring sounds, which can be effectively utilized for accurately identifying various snoring types in real-world scenarios. The dataset will be made publicly available for access at https://github.com/emleeee/ONEI.
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