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    Audio-based identification of beehive states 
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    Audio-based identification of beehive states

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    Accepted version (921.5Kb)
    Pagination
    ? - ? (5)
    Publisher
    IEEE
    Publisher URL
    https://2019.ieeeicassp.org/
    Metadata
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    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.
    Authors
    Nolasco, I; Terenzi, A; Cecchi, S; Orcioni, S; BEAR, H; BENETOS, E; IEEE International Conference on Acoustics, Speech, and Signal Processing
    URI
    https://qmro.qmul.ac.uk/xmlui/handle/123456789/55776
    Collections
    • Electronic Engineering and Computer Science [2315]
    Copyright statements
    © 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.
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