Snoring Event Detection Using Machine Learning Techniques

打鼾症是睡眠障碍的主要症状之一。国家睡眠基金会进行的调查**(1999-2004)显示，至少有4000万美国人患有70多种不同的睡眠障碍。**鼾声似乎被低估了，因为人们无法意识到他们鼾声的严重性，因为它发生在**睡眠中。整晚人工记录和检查人的呼吸声可能是一项非常耗时和依赖操作员的任务。因此，一种自动录音技术是**理想的。此外，市场上现有的检测鼾声的解决方案并不是为了解决**核心问题本身而设计的。我们的目标是在用户卧室使用我们的主动防鼾声**解决方案来诊断和解决鼾症的核心问题。几乎所有与打呼有关的产品都会与打鼾者进行身体接触，这会导致痛苦、不舒服或破坏性的体验。Smart Nora是市场上第一款帮助停止鼾症的非接触式、非侵入性**主动产品。虽然目前的产品在平均**场景下运行良好，但公司会收到一些基于当前产品的鼾声检测算法**的回报和负反馈。顾客经常抱怨该产品“没有捕捉到鼾声”，“激活太多”，或者在没有鼾声的情况下发出“错误警报”。这个项目将研究一种机器学习算法的开发和**实现，该算法旨在通过使用机器学习算法和先进的信号处理技术将鼾声事件与不同类型的**背景噪声区分开来。

Snoring is one of the major symptoms of a sleep disorder. Surveys conducted by the National Sleep Foundation**(1999-2004) have revealed that at least 40 million Americans suffer from over 70 different sleep disorders.**Snoring seems underestimated because people cannot recognize the seriousness of their snoring since it occurs**during sleep. Manual recording and examination of a person's respiratory sounds for the entire night can be a**very time-consuming and operator-dependent task. Therefore, an automatic sound recording technique is**desirable. Furthermore, existing solutions in the market for detection of snoring are not designed to address the**core issue itself. Our aim is to both diagnose and address the core issue of snoring using our active antisnoring**solution in the users' bedroom. Almost all of the snore related products make physical contact with the snorer**which result in painful, uncomfortable or disruptive experiences. Smart Nora is the first contact-free, non-invasive**active product on the market to help stop snoring. Although the current product works fine with average**scenarios, the company receives a number of returns and negative feedbacks based on snore detection algorithm**of the current product. Customers often complain about the product as 'not catching the snore', 'activating too**much', or giving 'false alarms' when there is no snore. This project will investigate the development and**implementation of a machine learning algorithm which aims to distinguish the snore events from different type**background noises by using machine learning algorithms and advanced signal processing techniques.