ArticleScientific data2020
An annotated dataset of bioacoustic sensing and features of mosquitoes.
Article in Scientific data, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- The evolution of mosquito baiting: from chemical and electronic methods toward AI.Parasites & vectors · 2026Review
- Leveraging geographic information system for dengue surveillance: a scoping review.Tropical medicine and health · 2025Review
- Acoustic behaviour and flight tone frequency changes in adult Aedes albopictus and Culex quinquefasciatus mosquitoes.Scientific reports · 2025Article
- Automated age grading of female Culex pipiens by an optical sensor system coupled to a mosquito trap.Parasites & vectors · 2024Article
- Reshaping Bioacoustics Event Detection: Leveraging Few-Shot Learning (FSL) with Transductive Inference and Data Augmentation.Bioengineering (Basel, Switzerland) · 2024Article
- Measuring factors affecting honey bee (Hymenoptera: Apidae) attraction to soybeans using bioacoustics monitoring.Journal of insect science (Online) · 2024Article
- Field evaluation of an automated mosquito surveillance system which classifies Aedes and Culex mosquitoes by genus and sex.Parasites & vectors · 2024Article
- Automated differentiation of mixed populations of free-flying female mosquitoes under semi-field conditions.Scientific reports · 2024Article
- A Review of Automated Bioacoustics and General Acoustics Classification Research.Sensors (Basel, Switzerland) · 2022Review
- A Low-Cost Multi-Purpose IoT Sensor for Biologging and Soundscape Activities.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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