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An annotated dataset of bioacoustic sensing and features of mosquitoes
As vectors of malaria, dengue, zika, and yellow fever, mosquitoes are considered one of the more severe worldwide health hazards. Widespread surveillance of mosquitoes is essential for understanding their complex ecology and behaviour, and also for predicting and formulating effective control strate...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7659005/ https://www.ncbi.nlm.nih.gov/pubmed/33177516 http://dx.doi.org/10.1038/s41597-020-00725-6 |
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author | Vasconcelos, Dinarte Nunes, Nuno Jardim Gomes, João |
author_facet | Vasconcelos, Dinarte Nunes, Nuno Jardim Gomes, João |
author_sort | Vasconcelos, Dinarte |
collection | PubMed |
description | As vectors of malaria, dengue, zika, and yellow fever, mosquitoes are considered one of the more severe worldwide health hazards. Widespread surveillance of mosquitoes is essential for understanding their complex ecology and behaviour, and also for predicting and formulating effective control strategies against mosquito-borne diseases. One technique involves using bioacoustics to automatically identify different species from their wing-beat sounds during flight. In this dataset, we collect sounds of three species of mosquitoes: Aedes Aegypti, Culex Quinquefasciatus & Pipiens, and Culiseta. These species were collected and reproduced in the laboratory of the Natural History Museum of Funchal, in Portugal, by entomologists trained to recognize and classify mosquitoes. For collecting the samples, we used a microcontroller and a mobile phone. The dataset presents audio samples collected with different sampling rates, where 34 audio features characterize each sound file, making it is possible to observe how mosquito populations vary heterogeneously. This dataset provides the basis for feature extraction and classification of flapping-wing flight sounds that could be used to identify different species. |
format | Online Article Text |
id | pubmed-7659005 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-76590052020-11-17 An annotated dataset of bioacoustic sensing and features of mosquitoes Vasconcelos, Dinarte Nunes, Nuno Jardim Gomes, João Sci Data Data Descriptor As vectors of malaria, dengue, zika, and yellow fever, mosquitoes are considered one of the more severe worldwide health hazards. Widespread surveillance of mosquitoes is essential for understanding their complex ecology and behaviour, and also for predicting and formulating effective control strategies against mosquito-borne diseases. One technique involves using bioacoustics to automatically identify different species from their wing-beat sounds during flight. In this dataset, we collect sounds of three species of mosquitoes: Aedes Aegypti, Culex Quinquefasciatus & Pipiens, and Culiseta. These species were collected and reproduced in the laboratory of the Natural History Museum of Funchal, in Portugal, by entomologists trained to recognize and classify mosquitoes. For collecting the samples, we used a microcontroller and a mobile phone. The dataset presents audio samples collected with different sampling rates, where 34 audio features characterize each sound file, making it is possible to observe how mosquito populations vary heterogeneously. This dataset provides the basis for feature extraction and classification of flapping-wing flight sounds that could be used to identify different species. Nature Publishing Group UK 2020-11-11 /pmc/articles/PMC7659005/ /pubmed/33177516 http://dx.doi.org/10.1038/s41597-020-00725-6 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article. |
spellingShingle | Data Descriptor Vasconcelos, Dinarte Nunes, Nuno Jardim Gomes, João An annotated dataset of bioacoustic sensing and features of mosquitoes |
title | An annotated dataset of bioacoustic sensing and features of mosquitoes |
title_full | An annotated dataset of bioacoustic sensing and features of mosquitoes |
title_fullStr | An annotated dataset of bioacoustic sensing and features of mosquitoes |
title_full_unstemmed | An annotated dataset of bioacoustic sensing and features of mosquitoes |
title_short | An annotated dataset of bioacoustic sensing and features of mosquitoes |
title_sort | annotated dataset of bioacoustic sensing and features of mosquitoes |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7659005/ https://www.ncbi.nlm.nih.gov/pubmed/33177516 http://dx.doi.org/10.1038/s41597-020-00725-6 |
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