Acoustic Identification of Ae. aegypti Mosquitoes using Smartphone Apps
  and Residual Convolutional Neural Networks
Kayuã Oleques Paim Ricardo Rohweder Mariana Recamonde-Mendoza Rodrigo Brandão Mansilha1 Weverton Cordeiro
In this paper, we advocate in favor of smartphone apps as low-cost,
easy-to-deploy solution 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 residual convolutional neural network for
classifying Ae. aegypti mosquitoes from their wingbeat sound, (ii) a
methodology for reducing the influence of background noise in the
classification process, and (iii) a dataset for benchmarking solutions for
detecting Ae. aegypti mosquitoes from wingbeat sound recordings. From the
analysis of accuracy and recall, we provide evidence that convolutional neural
networks have potential as a cornerstone for tracking mosquito apps for
smartphones.