Aedes Vigilance

Aedes Vigilance

Acoustic Identification of Ae. aegypti Mosquitoes using Smartphone Apps and Residual Convolutional Neural Networks

Elsevier Biomedical Signal Processing and Control

Our dataset is freely available for use under License Attribution 4.0 International (CC BY 4.0).

For citing this dataset, please use:

Kayuã Oleques Paim, Ricardo Rohweder, Mariana Recamonde-Mendoza, Rodrigo Brandão Mansilha, Weverton Cordeiro,
Acoustic identification of Ae. aegypti mosquitoes using smartphone apps and residual convolutional neural networks,
Biomedical Signal Processing and Control,
Volume 95, Part A,
2024,
106342,
ISSN 1746-8094,
https://doi.org/10.1016/j.bspc.2024.106342.

@article{PAIM2024106342, title = {Acoustic identification of Ae. aegypti mosquitoes using smartphone apps and residual convolutional neural networks}, journal = {Biomedical Signal Processing and Control}, volume = {95}, pages = {106342}, year = {2024}, issn = {1746-8094}, doi = {https://doi.org/10.1016/j.bspc.2024.106342}, url = {https://www.sciencedirect.com/science/article/pii/S1746809424004002}, author = {Kayuã Oleques Paim and Ricardo Rohweder and Mariana Recamonde-Mendoza and Rodrigo Brandão Mansilha and Weverton Cordeiro}, keywords = {, Audio classification, Audio analysis, Convolutional neural networks, Machine learning, Smartphone mosquito tracking app}, abstract = {In this paper, we advocate in favor of smartphone apps as low-cost, easy-to-deploy solutions for raising awareness among the population on the proliferation of Aedes aegypti mosquitoes. Nevertheless, devising such a smartphone app is challenging for many reasons, including the required maturity level of techniques for identifying mosquitoes based on features that can be captured using smartphone resources. In this paper, we identify a set of (non-exhaustive) requirements that smartphone apps must meet to become an effective tooling in the fight against Ae. aegypti, and advance the state-of-the-art with (i) a dataset for benchmarking solutions for detecting Ae. aegypti mosquitoes from wingbeat sound recordings collected with smartphones, and (ii) a residual convolutional neural network for classifying Ae. aegypti mosquitoes from their wingbeat sound, considering a training methodology for reducing the influence of background noise in the classification process. From the analysis of precision, accuracy, recall, and F1-score, we provide evidence that residual convolutional neural networks have potential as a cornerstone for tracking mosquito apps for smartphones. We demonstrate the proposed solution’s feasibility by implementing a prototype capable of detecting mosquitoes in a controlled environment.} }

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