| dc.contributor.author | Nolasco, I | en_US |
| dc.contributor.author | BENETOS, E | en_US |
| dc.contributor.author | 2018 Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE 2018) | en_US |
| dc.date.accessioned | 2018-11-30T15:06:42Z | |
| dc.date.available | 2018-09-17 | en_US |
| dc.date.issued | 2018-11-19 | en_US |
| dc.date.submitted | 2018-09-29T18:09:12.192Z | |
| dc.identifier.uri | http://qmro.qmul.ac.uk/xmlui/handle/123456789/53444 | |
| dc.description.abstract | 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. | en_US |
| dc.title | To bee or not to bee: Investigating machine learning approaches for beehive sound recognition | en_US |
| dc.type | Conference Proceeding | |
| dc.rights.holder | © The Author(s) 2018 | |
| pubs.notes | No embargo | en_US |
| pubs.publication-status | Accepted | en_US |
| pubs.publisher-url | http://dcase.community/workshop2018 | en_US |
| dcterms.dateAccepted | 2018-09-17 | 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 |
