AI 中文总结
本研究针对蜜蜂蜂王丢失的声学检测问题，比较了卷积神经网络与预训练音频变换器，发现基于调制频谱图的模型在未见蜂箱上取得最佳AUROC 0.81，但跨养蜂场泛化仍具挑战。
AI 中文摘要
蜜蜂蜂王丢失是对蜂群健康的主要威胁，然而蜂王状态的评估在很大程度上仍依赖人工且具有干扰性。声学监测通过持续分析蜂箱声音提供了一种非侵入性的替代方案。本文中，我们对用于自动检测蜂王缺失的常规与学习型声学表征进行了基准测试，比较了任务特定的卷积神经网络与预训练的音频变换器。实验在2024年10月至2026年8月期间收集的来自47个养蜂场、3285个蜂箱的5129段音频记录上进行，并采用蜂箱独立和养蜂场独立的划分进行评估。结果表明，依赖调制频谱图的模型达到了最佳性能，在未见蜂箱上实现了0.81的受试者工作特征曲线下面积（AUROC）和0.37的精确率-召回率曲线下面积（AUPRC）；在未见养蜂场评估下性能有所下降。总体而言，我们的结果凸显了基于调制的音频表征在非侵入性蜂王状态监测中的潜力，并强调了跨养蜂场模型泛化的挑战。
英文摘要
Honeybee queen loss is a major threat to colony health, yet queen-status assessment remains largely manual and disruptive. Acoustic monitoring offers a non-invasive alternative by enabling continuous analysis of hive sounds. In this paper, we benchmark conventional and learned acoustic representations for automated detection of queen absence, comparing task-specific convolutional neural networks with pretrained audio transformers. Experiments are performed on 5,129 audio recordings from 3,285 hives across 47 apiaries collected between October 2024 and August 2026, evaluated using hive- and apiary-independent splits. Results show that models relying on modulation spectrograms achieve the best performance, reaching an Area Under the Receiver Operating Characteristic (AUROC) of 0.81 and Area Under the Precision-Recall Curve (AUPRC) of 0.37 on unseen hives; performance decreases under unseen-apiary evaluation. Overall, our results highlight the promise of modulation-based audio representations for non-invasive queen-status monitoring and highlight the challenge in cross-apiary model generalization.
