<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Inês Nolasco</style></author><author><style face="normal" font="default" size="100%">Emmanouil Benetos</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">To bee or not to bee: Investigating machine learning approaches for beehive sound recognition</style></title><secondary-title><style face="normal" font="default" size="100%">arXiv</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">beehive sound recognition</style></keyword><keyword><style  face="normal" font="default" size="100%">Computational bioacoustic scene analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">ecoacoustics</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://arxiv.org/pdf/1811.06016.pdf</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In this work, we aim to explore the potential of machine learning methods to the problem of beehive sound recognition. A major contribution of this work is the creation and release of annotations for a selection of beehive recordings. By experimenting with both support vector machines and convolutional neural networks, we explore important aspects to be considered in the development of beehive sound recognition systems using machine learning approaches.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Inês Nolasco</style></author><author><style face="normal" font="default" size="100%">Alessandro Terenzi</style></author><author><style face="normal" font="default" size="100%">Stefania Cecchi</style></author><author><style face="normal" font="default" size="100%">Simone Orcioni</style></author><author><style face="normal" font="default" size="100%">Helen L. Bear</style></author><author><style face="normal" font="default" size="100%">Emmanouil Benetos</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Audio-based identification of beehive states</style></title><secondary-title><style face="normal" font="default" size="100%">arXiv</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://arxiv.org/abs/1811.06330</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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.&lt;/p&gt;
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