WindDancer: Understanding Acoustic Sensing under Ambient Airflow

Abstract

Acoustic sensing has recently garnered significant interest for a wide range of applications ranging from motion tracking to health monitoring. However, prior works overlooked an important real-world factor affecting acoustic sensing systems---the instability of the propagation medium due to ambient airflow. Airflow introduces rapid and random fluctuations in the speed of sound, leading to performance degradation in acoustic sensing tasks. This paper presents WindDancer, the first comprehensive framework to understand how ambient airflow influences existing acoustic sensing systems, as well as provides solutions to enhance systems performance in the presence of airflow. Specifically, our work includes a mechanistic understanding of airflow interference, modeling of sound speed variations, and analysis of how several key real-world factors interact with airflow. Furthermore, we provide practical recommendations and signal processing solutions to improve the resilience of acoustic sensing systems for real-world deployment. We envision that WindDancer establishes a theoretical foundation for understanding the impact of airflow on acoustic sensing, and advances the reliability of acoustic sensing technologies for broader adoption.