Automatic Mosquito Surveillance and Visualisation using Acoustic Signals

Ayesha Hakim


Mosquitoes are considered to be among the biggest disease spreading flying insects causing worldwide health hazards.  In Pakistan, Malaria and Dengue fever are among the most dangerous infectious viral diseases transmitted through the bite of infected Anopheles and Aedes mosquitoes. Manual identification of these mosquitoes is hard and dangerous leading to severe health risks. This paper presents an automated system based on audio analysis and IoT sensors to identify and classify mosquito species including Aedes (transmits dengue), Anopheles (transmits Malaria), and Culex (transmits viral diseases including avian malaria) species using the acoustic recordings of their wingbeat frequency. Computational analysis of these audio signals leads to inter-species classification and behavioral study of different mosquito species under varying environmental conditions.  We demonstrate the proposed approach on a standard dataset and compare it to human annotations, with promising results.

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