@article {53336,
	title = {Audio-based identification of beehive states},
	journal = {arXiv},
	year = {2018},
	abstract = {<p>The absence of the queen in a beehive is a very strong indicator of the need for beekeeper intervention. Manually searching for the queen is an arduous recurrent task for beekeepers that disrupts the normal life cycle of the beehive and can be a source of stress for bees. Sound is an indicator for signalling different states of the beehive, including the absence of the queen bee. In this work, we apply machine learning methods to automatically recognise different states in a beehive using audio as input. \% The system is built on top of a method for beehive sound recognition in order to detect bee sounds from other external sounds. We investigate both support vector machines and convolutional neural networks for beehive state recognition, using audio data of beehives collected from the NU-Hive project. Results indicate the potential of machine learning methods as well as the challenges of generalizing the system to new hives.</p>
},
	url = {https://arxiv.org/abs/1811.06330},
	author = {In{\^e}s Nolasco and Alessandro Terenzi and Stefania Cecchi and Simone Orcioni and Helen L. Bear and Emmanouil Benetos}
}
