用合成数据模拟蚊群声音，实现高效蚊种识别。
Synthetic Swarm Mosquito Dataset for Acoustic Classification: A Proof of Concept
- 通过合成音频生成多物种嘈杂蚊群数据。
- 模型可准确识别六种主要传病蚊种。
- 适合嵌入式低功耗设备实时监测。
蚊媒疾病每年导致超过70万例死亡，构成重大全球健康威胁。本文提出一种用于声学分类的合成群体蚊虫数据集概念验证方案，旨在模拟真实多物种、高噪声的蚊群环境。与传统依赖人工逐个录制蚊子声音的数据集不同，该合成方法可规模化生成数据，显著降低人力成本。采用对数梅尔频谱图作为输入，评估了轻量级深度学习架构在蚊种分类中的表现。实验表明，这些模型能有效识别六种主要传病蚊种，且适用于部署于嵌入式低功耗设备。研究证明，合成群体音频数据集具有加速声学蚊类研究的潜力，并可支持可扩展的实时监测应用。
原文摘要 · Abstract (English)
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.
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