Systems and methods of using a machine learning model to classify catalytic converter theft tamper events based on audio data

Abstract

Examples provide systems and methods for detecting vehicle tamper events without relying on a clear line-of-sight. Namely, examples leverage an intelligent insight that many types of vehicle tamper events have unique audio signatures. Accordingly, examples detect/classify vehicle tamper events based on these unique audio signatures. Moreover, examples can verify these audio-based classifications by analyzing acceleration-related data (e.g., relative acceleration data for a body of a vehicle, relative jerk data for a body of a vehicle, etc.) to determine suspicious movement of a body of a vehicle during a potential/suspected vehicle tamper event. This acceleration-related verification step can reduce occurrence of false positive audio-based classifications caused by other noise events proximate to the vehicle that have similar audio signatures to vehicle tamper events (e.g., drilling or other noise from a construction site, rain, etc.).