Mosquito acoustic identification at unseen recording sites: practical limits of common transfer remedies for vector surveillance
Yucheng Gong, Rui Zhou, Binbin Zeng, Qiang Ren, Hongjin Hui
Passive acoustic monitoring could widen mosquito surveillance, but a classifier trained under one set of recording conditions often fails when moved to a new site, device or enclosure. On a nine-species, five-condition corpus of about 271,000 recordings, the published baseline identifies species at 0.881 balanced accuracy under conditions it has seen and 0.175 under held-out ones. Using that corpus's leave-domain protocol, we asked which transfer strategy is most reliable when a program brings a new site online. Using fixed features from a pre-trained bioacoustic model and a small classifier improved held-out-condition balanced accuracy to 0.305, without updating backbone weights and in minutes of training per site. Four widely used additions did not improve transfer: rebalancing training across recording conditions, fine-tuning the backbone and fusing a weaker second modality each reduced accuracy, and a six-term objective reduced to a single alignment term. Rebalancing reversed direction across four encoders, helping weak representations and harming strong ones. Genus-level aggregation was more accurate than species-level classification (about 0.50 against 0.30), suggesting genus-level screening may be the more realistic operating point here. These results come from one corpus under one protocol, and are dataset-bounded guidance rather than validated general rules.
尚无 AI 解读，展示原文摘要