dc.contributor.author | Nolasco, I | |
dc.contributor.author | Terenzi, A | |
dc.contributor.author | Cecchi, S | |
dc.contributor.author | Orcioni, S | |
dc.contributor.author | BEAR, H | |
dc.contributor.author | BENETOS, E | |
dc.contributor.author | IEEE International Conference on Acoustics, Speech, and Signal Processing | |
dc.date.accessioned | 2019-03-05T10:15:20Z | |
dc.date.available | 2019-02-01 | |
dc.date.available | 2019-03-05T10:15:20Z | |
dc.date.issued | 2019-05-12 | |
dc.identifier.citation | Nolasco, I., Terenzi, A., Cecchi, S., Orcioni, S., Bear, H. and Benetos, E. (2019). Audio-based identification of beehive states. [online] arXiv.org. Available at: https://arxiv.org/abs/1811.06330 [Accessed 5 Mar. 2019]. | en_US |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/55776 | |
dc.description.abstract | 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. 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. | en_US |
dc.format.extent | ? - ? (5) | |
dc.publisher | IEEE | en_US |
dc.title | Audio-based identification of beehive states | en_US |
dc.type | Conference Proceeding | en_US |
dc.rights.holder | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |
pubs.notes | No embargo | en_US |
pubs.notes | IEEE conference, allows publishing postprints at institutional repositories. | en_US |
pubs.publication-status | Accepted | en_US |
pubs.publisher-url | https://2019.ieeeicassp.org/ | en_US |
dcterms.dateAccepted | 2019-02-01 | |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |
qmul.funder | A Machine Learning Framework for Audio Analysis and Retrieval::Royal Academy of Engineering | en_US |
qmul.funder | A Machine Learning Framework for Audio Analysis and Retrieval::Royal Academy of Engineering | en_US |