01000nas a2200145   4500008004100000245009800041210006900139520047100208653003000679653004500709653001700754100001900771700002300790856004100813       2018                        eng d00aTo bee or not to bee: Investigating machine learning approaches for beehive sound recognition0 aTo bee or not to bee Investigating machine learning approaches f3 a<p>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.</p>
10abeehive sound recognition10aComputational bioacoustic scene analysis10aecoacoustics1 aNolasco, Inês1 aBenetos, Emmanouil  uhttps://arxiv.org/pdf/1811.06016.pdf01412nas a2200157   4500008004100000245004900041210004800090520095200138100001901090700002401109700002101133700002001154700002001174700002301194856003701217       2018                        eng d00aAudio-based identification of beehive states0 aAudiobased identification of beehive states3 a<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>
1 aNolasco, Inês1 aTerenzi, Alessandro1 aCecchi, Stefania1 aOrcioni, Simone1 aBear, Helen, L.1 aBenetos, Emmanouil  uhttps://arxiv.org/abs/1811.06330