提升蚊子声音分类跨域能力，避免模型依赖录音环境干扰。
Learning Domain-Robust Bioacoustic Representations for Mosquito Species Classification with Contrastive Learning and Distribution Alignment
- 用对比学习和分布对齐消除环境差异干扰
- 跨域测试准确率显著优于基线方法
- 适合真实场景中需跨地区部署的蚊子监测
蚊子物种分类对病媒监测与疾病防控至关重要。由于蚊子活动季节和实地采集限制，生物声学数据收集困难。不同区域、栖息地和实验室的蚊子录音常包含非生物性环境差异，即领域特征。本研究发现，直接在含领域特征的音频上训练的模型会依赖环境信息而非物种声学特征进行识别，导致看似表现良好却跨域泛化差。为此，提出领域鲁棒生物声学学习（DR-BioL）框架，结合对比学习与条件分布对齐。对比学习增强同物种内部一致性并减小域间差异，物种条件分布对齐进一步提升跨域物种表征。在多域蚊子生物声学数据集上的实验表明，DR-BioL显著优于基线，在多样环境中实现更优准确率与鲁棒性，展现出真实世界中可靠跨域分类的潜力。
原文摘要 · Abstract (English)
Mosquito Species Classification (MSC) is crucial for vector surveillance and disease control. The collection of mosquito bioacoustic data is often limited by mosquito activity seasons and fieldwork. Mosquito recordings across regions, habitats, and laboratories often show non-biological variations from the recording environment, which we refer to as domain features. This study finds that models directly trained on audio recordings with domain features tend to rely on domain information rather than the species' acoustic cues for identification, resulting in illusory good performance while actually performing poor cross-domain generalization. To this end, we propose a Domain-Robust Bioacoustic Learning (DR-BioL) framework that combines contrastive learning with distribution alignment. Contrastive learning aims to promote cohesion within the same species and mitigate inter-domain discrepancies, and species-conditional distribution alignment further enhances cross-domain species representation. Experiments on a multi-domain mosquito bioacoustic dataset from diverse environments show that the DR-BioL improves the accuracy and robustness of baselines, highlighting its potential for reliable cross-domain MSC in the real world.
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