Abstract

How do you distinguish snoring sounds from two or more people sharing a bedroom? And why is it important? With several million users choosing to share their data with Sleep Cycle, we are in a unique and privileged position to deepen our understanding of the impact snoring can have on our sleep quality and, by extension, our well-being. Our vast dataset has allowed us to train a snore embedding network that can differentiate between different people's snoring, regardless of how many people or pets(!) are sharing a bedroom.

Snoring, often dismissed as a nuisance only to the person sharing the room, can be a sign of obstructive sleep apnea and is linked to a wide range of other negative health-related issues. Providing the user with a comprehensive sleep analysis and letting them know where the snoring is coming from is the first step for them to implement the proper measures for their sleep health and overall well-being.

Maria Larsson

Machine Learning Engineer @ Sleep Cycle

Maria Larsson is a leading machine learning engineer in the Sleep Cycle R&D team that develops the market-leading sleep tracking solution worldwide, with millions of daily active users and over two billion nights analyzed in more than 150 countries. Sleep Cycle aims to improve global health by empowering people to sleep better. Apple recently recognized Maria for her outstanding contributions to the iOS app economy as part of their "Simply Outstanding Women" segment.