EchoSense: Acoustic Sensing for Enclosed Structure Monitoring and Recognition

Audio-based shape recognition is often used in robotics and navigation. With the popularity of in-ear earphones, tiny robots, and drones with low-power microphones, we see an opportunity where acoustic sensing can be used to monitor small enclosed spaces, e.g., ear canals, pipes, and machinery, for humans, infrastructure, and machine health monitoring. This paper studies the opportunity of using acoustic sensing to identify and monitor various small hollow objects from the inside for preventive machine maintenance during downtime. We propose an algorithm that fuses signal processing and machine learning techniques to distinguish between three shapes and assess the health of the shapes. Our experimental result show that different set of acoustic features distinguish object's shape with 80% accuracy. These features can further identify the existence of deformation with 100% accuracy and localize the deformation with 90% accuracy.