提出生态感知的嵌套交叉验证与抗噪注意力模型，提升海洋生物声学监测可靠性。
GetNetUPAM: Ecologically Informed Nested Cross-Validation and Noise-Robust Attention for Marine Bioacoustic Monitoring
- 采用分层嵌套交叉验证，按站点-年份划分数据，模拟真实部署环境
- 在零训练支持的巴伦支岛区域，误报率降低约10倍，召回率保持90%
- 通过可学习注意力机制抑制噪声，避免模型依赖非生物线索
部署可靠的生物声学监测系统需应对高噪声、低信噪比条件，而当前UPAM实践在模型泛化与评估协议上存在显著空白。固有噪声、传播变化及生物与人为声源混合导致分布偏移，传统模型与单次分割评估会掩盖不稳定性并虚高性能。本文提出GetNetUPAM，一种分层嵌套交叉验证框架，以量化模型稳定性而非优化虚假测试得分。通过将数据按站点-年份分块，保留生态异质性，使每个外层折叠代表不同环境状况，防止对局部噪声或传感器伪影过拟合；内层分层折叠则衡量在完整信号分布下的泛化能力，严格分离模型开发与外层部署条件。基于此框架，评估了自适应分辨率池化与注意力网络（ARPA-N），一种用于不规则频谱图的CNN架构。ARPA-N集成CBAM空间注意力作为可学习噪声抑制器，生成聚焦真实叫声结构的注意力图，避免标准CNN在长窗数据中利用全局非生物线索。在GetNetUPAM下，ARPA-N在多种环境条件下均表现出稳健泛化能力。在无训练支持的巴伦支群岛区域，固定90%召回率下，每小时误报率降低约10倍，各折叠表现持续优于基准。该工作提供可复现基准，推动UPAM向可扩展、部署可靠生态监测迈进。
原文摘要 · Abstract (English)
Deploying reliable bioacoustic monitoring systems requires models that generalize under high-noise, low-SNR conditions and evaluation protocols that expose deployment-relevant failure modes, gaps largely unaddressed in current UPAM practice. Intrinsic noise, variable propagation, and mixed biological and anthropogenic sources induce distribution shifts that conventional models and single-split evaluations obscure, inflating performance and masking instability. We introduce GetNetUPAM, a hierarchical nested cross-validation framework that uses the nested stage to quantify model stability rather than tune for inflated hold-out scores. By partitioning data into site-year blocks, GetNetUPAM preserves ecological heterogeneity and forces each outer fold to represent a distinct environmental regime, preventing overfitting to localized noise or sensor artifacts. Inner stratified folds measure generalization across the full UPAM signal distribution, enforcing strict separation between model development and the outer held-out deployment condition. Using GetNetUPAM, we evaluate the Adaptive Resolution Pooling and Attention Network (ARPA-N), a CNN architecture for irregular spectrogram dimensions. ARPA-N integrates CBAM spatial attention as a learned noise suppressor, producing attention maps that localize true call structure and avoid the global, non-biological cues exploited by standard CNNs on long-window data. Under GetNetUPAM, ARPA-N generalizes robustly across diverse environmental regimes. In the zero-training support Balleny Islands region, it reduces false positives per hour by over an order of magnitude (approximately 10x) at fixed 90 percent recall, yielding consistently improved metrics across folds. These advances provide a reproducible benchmark and move UPAM toward scalable, deployment-reliable ecological monitoring.
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