Discovery and promotion of unknown sounds into operational detection targets for underwater passive acoustic monitoring under false alarm constraints

Earth’s oceans cover 70% of the planet yet remain among the least observed regions. Underwater acoustics is a primary sensing modality, but recordings span biological, anthropogenic and natural sounds while labels remain sparse. We present an unknownness-aware monitoring framework for underwater acoustic sensing platforms: it adapts a pre-trained audio representation model to underwater soundscapes, defines an embedding-space unknownness score, and couples it with detector outputs in a Detect–Group–Promote–Union (DGPU) loop tuned under false positives per hour (FP/h) constraints: Detect surfaces candidate events from continuous audio; Group clusters unknown candidates in embedding space; Promote trains lightweight classifiers from sparse expert labels on selected clusters; and Union combines baseline and promoted detectors. Across a 56-class underwater Sound Event Detection (SED) benchmark and two cetacean field datasets, the framework attains a dataset-level bandwidth-matched out-of-distribution AUROC of 1.000 on Hawaiian survey data (HICEAS), raises right-whale upcall detection recall on continuous recordings (FRDR) from 0.027 to 0.390 at FP/h ≈ 10 with a supervised Promoter, with 300 labelled events (~ 32% of training-fold labels) reaching 0.37 (95% of the full-label recall), and classifies seven HICEAS cetacean species with the Promoter classifier alone (AUC 0.924–0.992 within-day and 0.51–0.97 cross-day with a fixed classifier), supporting stethoscope-like ocean monitoring.