Acoustic Drone Package Delivery Detection

Abstract

In recent years, the illicit use of un- manned aerial vehicles (UAVs) for deliveries in re- stricted area such as prisons became a significant security challenge. While numerous studies have fo- cused on UAV detection or localization, little atten- tion has been given to delivery events identification. This study presents the first acoustic package deliv- ery detection algorithm using a ground-based micro- phone array. The proposed method estimates both the drone’s propeller speed and the delivery event using solely acoustic features. A deep neural network detects the presence of a drone and estimates the propeller’s rotation speed or blade passing frequency (BPF) from a mel spectrogram. The algorithm ana- lyzes the BPFs to identify probable delivery moments based on sudden changes before and after a specific time. Results demonstrate a mean absolute error of the blade passing frequency estimator of 16 Hz when the drone is less than 150 meters away from the microphone array. The drone presence detection esti- mator has a accuracy of 97%. The delivery detection algorithm correctly identifies 96% of events with a false positive rate of 8 %. This study shows that deliveries can be identified using acoustic signals up to a range of 100 meters.