ABSTRACT Effective conservation of the endangered Cook Inlet beluga whale ( Delphinapterus leucas ) requires comprehensive spatiotemporal data, yet monitoring efforts remain spatially biased, underrepresenting important southern habitats. Passive acoustic monitoring (PAM) provides the necessary broad‐scale coverage, but its expansion introduces substantial computational challenges, including high soundscape variability, rarity of target species' signals relative to background noise, and multi‐species signal interference that can compromise classifier performance. We present an open‐source deep learning framework designed to improve robustness, adaptability, and domain generalization of PAM analyses for this population. Building on a beluga‐focused binary classifier, we implemented a dual‐stage model that separates signal detection from species classification and expanded the framework to a multi‐species context that includes killer whales ( Orcinus orca ) and humpback whales ( Megaptera novaeangliae ). Contrastive audio‐language models were used to efficiently increase annotation coverage for previously underrepresented species, while active learning enabled iterative refinement of model performance on new data. The framework was applied to PAM datasets from management and ecologically significant regions of lower Cook Inlet. Results demonstrated improved detection of rare species occurrences and increased confidence in daily beluga presence estimates, strengthening the role of PAM in informing recovery efforts and management decisions. This transferable workflow supports continued advancement of large‐scale, long‐term marine mammal monitoring programs.
Castellote et al. (Sun,) studied this question.