In recent years, the illicit use of unmanned aerial vehicles (UAVs) for deliveries in restricted area such as prisons became a significant security challenge. While numerous studies have focused on UAV detection or localization, little attention has been given to delivery events identification. This study presents the first acoustic package delivery detection algorithm using a ground-based microphone 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 analyzes 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 estimator 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.

翻译：近年来，无人机在监狱等管制区域被非法用于投递物品，已成为一项重大的安全挑战。尽管已有大量研究专注于无人机检测或定位，但对投递事件识别却关注甚少。本研究提出了首个利用地面麦克风阵列进行声学包裹投递检测的算法。该方法仅利用声学特征，同时估计无人机的螺旋桨转速和投递事件。一个深度神经网络从梅尔频谱图中检测无人机的存在并估计螺旋桨的旋转速度或叶片通过频率。该算法通过分析叶片通过频率，根据特定时间前后发生的突变来识别可能的投递时刻。结果表明，当无人机距离麦克风阵列小于150米时，叶片通过频率估计器的平均绝对误差为16赫兹。无人机存在检测估计器的准确率达到97%。投递检测算法能正确识别96%的事件，误报率为8%。本研究表明，利用声学信号可在最远100米的范围内识别投递行为。