Abstract
In terms of vectoring disease, mosquitoes are the world’s deadliest.
A fast and efficient mosquito survey tool is crucial for vectored disease intervention programmes to reduce mosquito-induced deaths.
Standard mosquito sampling techniques, such as human landing
catches, are time consuming, expensive and can put the collectors
at risk of diseases. Mosquito acoustic detection aims to provide a
cost-effective automated detection tool, based on mosquitoes’ characteristic flight tones. We propose a simple, yet highly effective,
classification pipeline based on the mel-frequency spectrum allied
with convolutional neural networks. This detection pipeline is computationally efficient in not only detecting mosquitoes, but also in
classifying species. Many previous assessments of mosquito acoustic detection techniques have relied only upon lab recordings of
mosquito colonies. We illustrate in this paper our proposed algorithm’s performance over an extensive dataset, consisting of cup
recordings of more than 1000 mosquito individuals from 6 species
captured in field studies in Thailand.