发表机构
Microsoft AI for Good Research Lab; Universidad de los Andes（微软AI公益研究院; 安第斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对虎鲸被动声学监测的需求，提出基于ResNet的两阶段级联模型，在DCLDE 2027数据集上实现高精度检测与分类，可适配新声学域且推理速度快，适用于虎鲸保护的实时监测。
AI 中文摘要
虎鲸的被动声学监测对濒危的南方居留型虎鲸种群保护至关重要，但需要能在严重类别不平衡和部署偏移场景下实时运行的准确模型。我们提出一种轻量级基于ResNet的两阶段级联，首先检测虎鲸发声，再将置信度高的检测结果分类为5种东北太平洋生态型，对模糊叫声弃权（不执行）。我们在DCLDE 2027数据集上训练并评估该流水线，其中检测器达到0.960的宏F1值，分类器达到0.958，在5种生态型基准上优于冻结的Perch 2.0嵌入。通过将检测与生态型识别分离，端到端级联使七类宏F1从单阶段模型的0.919提升至0.933，在稀有OKW生态型上的提升最大。为评估基准外的迁移能力，我们使用主动学习使Stage 1适配华盛顿州普吉特湾的声学环境，在人工验证的检测窗口上虎鲸检测F1从0.405提升至0.755。最后，在NVIDIA H100上，每个阶段处理一个3秒窗口约需1.4毫秒，实现快于实时的推理。这些结果表明，所提出的两阶段级联流水线可实现可靠的虎鲸检测与分类、对新声学域的适配，以及适用于保护应用的实时监测。
英文摘要
Passive acoustic monitoring of killer whales is particularly important for conservation of the endangered Southern Resident killer whale population, but requires accurate models that can operate in real time under severe class imbalance and deployment shift. We propose a lightweight ResNet-based two-stage cascade that first detects killer whale vocalizations and then classifies confident detections into five eastern North Pacific ecotypes, abstaining on ambiguous calls. We train and evaluate the pipeline on the DCLDE 2027 dataset, where the detector achieves 0.960 macro-F1 and the classifier 0.958, outperforming frozen Perch 2.0 embeddings on the five-ecotype benchmark. By separating detection from ecotype recognition, the end-to-end cascade improves seven-class macro-F1 from 0.919 for a single-stage model to 0.933, with the largest gain on the rare OKW ecotype. To assess transfer beyond the benchmark, we use active learning to adapt the Stage 1 to the acoustic environment of Puget Sound, WA, increasing killer whale detection F1 from 0.405 to 0.755 on manually verified detection windows. Finally, each stage processes a 3 s window in approximately 1.4 ms on an NVIDIA H100, enabling faster than real time inference. These results demonstrate that the proposed two-stage cascade pipeline enables reliable killer whale detection and classification, adaptation to new acoustic domains, and real-time monitoring for conservation applications.
