Detecting Aedes aegypti mosquitoes through audio classification with convolutional neural networks

https://doi.org/10.1016/j.compbiomed.2020.104152Get rights and content
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Highlights

  • CNN-based models can identify Aedes aegypti from sounds captured with smartphones.

  • The studied models reached an accuracy as high as 97% and F1-measure of 89%.

  • An ensemble model had the best sensitivity (96%) in the detection of Aedes aegypti.

  • These results suggest the potential of detecting Aedes aegypti using smartphone apps.

  • Such apps could raise awareness and help fight nearby Aedes aegypti breeding sites.

Abstract

The incidence of mosquito-borne diseases is significant in under-developed regions, mostly due to the lack of resources to implement aggressive control measurements against mosquito proliferation. A potential strategy to raise community awareness regarding mosquito proliferation is building a live map of mosquito incidences using smartphone apps and crowdsourcing. In this paper, we explore the possibility of identifying Aedes aegypti mosquitoes using machine learning techniques and audio analysis captured from commercially available smartphones. In summary, we downsampled Aedes aegypti wingbeat recordings and used them to train a convolutional neural network (CNN) through supervised learning. As a feature, we used the recording spectrogram to represent the mosquito wingbeat frequency over time visually. We trained and compared three classifiers: a binary, a multiclass, and an ensemble of binary classifiers. In our evaluation, the binary and ensemble models achieved accuracy of 97.65% (±0.55) and 94.56% (±0.77), respectively, whereas the multiclass had an accuracy of 78.12% (±2.09). The best sensitivity was observed in the ensemble approach (96.82% ± 1.62), followed by the multiclass for the particular case of Aedes aegypti (90.23% ± 3.83) and the binary (88.49% ± 6.68). The binary classifier and the multiclass classifier presented the best balance between precision and recall, with F1-measure close to 90%. Although the ensemble classifier achieved the lowest precision, thus impairing its F1-measure (79.95% ± 2.13), it was the most powerful classifier to detect Aedes aegypti in our dataset.

Keywords

Aedes aegypti
Audio classification
Audio analysis
Convolutional neural networks
Machine learning

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