@ARTICLE{Libal_Urszula_Drone_Online,
 author={Libal, Urszula and Biernacki, Pawel},
 journal={Archives of Acoustics},
 howpublished={online},
 year={Online first},
 publisher={Polish Academy of Sciences, Institute of Fundamental Technological Research, Committee on Acoustics},
 abstract={Spotting a significant number of drones flying near the entrance of a beehive during late Spring could indicate the occurrence of swarming mood, as the the surge in drone presence is related to an overcrowded hive. Swarming refers to a natural reproductive process witnessed in honey bees, wherein half of the bee colony departs from their hive alongside the aging queen. In the paper, we propose an early swarming detection mechanism that relies on the behavior of the drones. The proposed method is based on audio signals registered in a close proximity to the beehive entrance. A comparative study was performed to find the most effective preprocessing method for the audio signals for the detection problem. We have compared the results for three different power spectrum density coefficients estimation methods, which are used as an input of an autoencoder neural network to discriminate drones from worker bees. Through simulations employing real-life signals, it has been demonstrated that drone detection based solely on audio signals is indeed feasible. The attained level of detection accuracy enables the creation of an efficient alarm system for beekeepers.},
 type={Article},
 title={Drone Flight Detection at an Entrance to a Beehive Based on Audio Signals},
 URL={http://czasopisma.pan.pl/Content/132089/PDF-MASTER/aoa.2024.148796.pdf},
 doi={10.24425/aoa.2024.148796},
 keywords={signal processing, machine learning, neural networks, anomaly detection, autoencoder, honey beeswarming, drone detection},
}