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.

在不发达地区，蚊媒疾病的发病率很高，这主要是由于缺乏用于对付蚊子扩散进行积极控制措施的资源。增强社区对蚊子扩散意识的一项潜在策略是使用智能手机应用程序和众包构建实时的蚊子发病图。在本文中，我们探讨了使用机器学习技术和从市售智能手机捕获的音频分析来识别埃及伊蚊的可能性。总而言之，我们对埃及伊蚊进行了下采样wingbeat录音，并通过监督学习将其用于训练卷积神经网络（CNN）。作为一项功能，我们使用记录频谱图来直观地表示随时间变化的蚊子拍打频率。我们训练并比较了三个分类器：一个二进制分类器，一个多分类器和一组二进制分类器。在我们的评估中，二元模型和集成模型的准确度分别为97.65％（±0.55）和94.56％（±0.77），而多类模型的准确度为78.12％（±2.09）。在合奏法中观察到最佳灵敏度（96.82％±1.62），其次是埃及伊蚊的多类（90.23％±3.83）和二进制（88.49％±6.68）。二进制分类器和多类分类器在精度和查全率之间取得了最佳平衡，F1量度接近90％。尽管整体分类器的精度最低，从而损害了其F1量度（79.95％±2.13），但它是检测我们数据集中埃及伊蚊的最强大分类器。