From manual analysis to automation: scaling passive acoustic monitoring for the endangered Saimaa ringed seals

Abstract

Passive acoustic monitoring (PAM) offers a non-invasive method for monitoring elusive pinnipeds, but manual analysis of large recording datasets limits its scalability. For the endangered Saimaa ringed seal ( Pusa saimensis ), PAM provides a rare opportunity to study breeding-season behavior beneath seasonal ice cover. We evaluated automated methods for detecting and characterizing the species' distinctive knocking vocalizations using recordings from Lake Saimaa. Annotated data ( n = 12 565 calls) were used to develop pulse repetition rate (PRR) estimation and call-detection systems. The best-performing PRR estimator matched manual measurements with a mean absolute error of 1.50 Hz, while a spectrogram-based convolutional neural network detected knocking calls with a mean F1-score of up to 99.28%. These results show close agreement with manual approaches, indicating that automated detection and characterization are achievable at a standard that could substantially reduce PAM analysis effort. This represents an important step toward scalable, long-term acoustic monitoring of this endangered seal.