Synthetic Swarm Mosquito Dataset for Acoustic Classification: A Proof of Concept
This proof-of-concept addresses the need for scalable real-time surveillance solutions for mosquito-borne diseases, which cause over 700,000 deaths annually, but it is incremental as it builds on existing acoustic classification methods with synthetic data.
This work tackled the problem of mosquito-borne diseases by creating a synthetic swarm mosquito dataset for acoustic classification, enabling scalable data generation and reducing human resource demands. The result showed that lightweight deep learning models can effectively identify six major mosquito vectors and are suitable for deployment on embedded low-power devices.
Mosquito-borne diseases pose a serious global health threat, causing over 700,000 deaths annually. This work introduces a proof-of-concept Synthetic Swarm Mosquito Dataset for Acoustic Classification, created to simulate realistic multi-species and noisy swarm conditions. Unlike conventional datasets that require labor-intensive recording of individual mosquitoes, the synthetic approach enables scalable data generation while reducing human resource demands. Using log-mel spectrograms, we evaluated lightweight deep learning architectures for the classification of mosquito species. Experiments show that these models can effectively identify six major mosquito vectors and are suitable for deployment on embedded low-power devices. The study demonstrates the potential of synthetic swarm audio datasets to accelerate acoustic mosquito research and enable scalable real-time surveillance solutions.