Snoring is one of the most prominent symptoms of Obstructive Sleep Apnea-Hypopnea Syndrome (OSAH), a highly prevalent disease that causes repetitive collapse and cessation of the upper airway. Thus, accurate snore sound monitoring and analysis is crucial. However, the traditional monitoring method polysomnography (PSG) requires the patients to stay at a sleep clinic for the whole night and be connected to many pieces of equipment. An alternative and less invasive way is passive monitoring using a smartphone at home or in the clinical settings. But, there is a challenge: the environment may be shared by people such that the raw audio may contain the snore activities of the bed partner or other person. False capturing of the snoring activity could lead to critical false alarms and misdiagnosis of the patients. To address this limitation, we propose a hypothesis that snore sound contains unique identity information which can be used for user recognition. We analyzed various machine learning models: Gaussian Mixture Model (GMM), GMM-UBM (Universial Background Model), and a Deep Neural Network (DNN) on MPSSC - an open source snoring dataset to evaluate the validity of our hypothesis. Our results are promising as we achieved around 90% accuracy in identification and verification tasks. This work marks the first step towards understanding the practicality of snore based user monitoring to enable multiple healthcare applicaitons.

翻译：打鼾是阻塞性睡眠呼吸暂停低通气综合征（OSAH）最显著的症状之一。OSAH是一种高发性疾病，会导致上气道反复塌陷和阻塞。因此，准确的鼾声监测与分析至关重要。然而，传统的监测方法——多导睡眠监测（PSG）要求患者整夜待在睡眠诊所并连接大量设备。一种替代且侵入性较小的方法是使用智能手机在家或临床环境中进行被动监测。但这面临一个挑战：环境中可能有多人共享，因此原始音频可能包含床伴或其他人的鼾声活动。错误捕获鼾声活动可能导致严重的误报和患者误诊。为解决这一局限，我们提出假设：鼾声包含可用于用户识别的独特身份信息。我们在MPSSC（一个开源鼾声数据集）上分析了多种机器学习模型：高斯混合模型（GMM）、GMM-UBM（通用背景模型）和深度神经网络（DNN），以评估该假设的有效性。我们的结果令人鼓舞，在识别和验证任务中达到了约90%的准确率。这项工作标志着理解基于鼾声的用户监测实用性、从而实现多种医疗应用的第一步。