Detecting ringed seal vocalizations using deep learning
Date
2024
Authors
Zammit, Karlee Elizabeth
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Abstract
Ringed seals (Pusa hispida), a species of Arctic seal, were assessed as a species of special concern in Canada due to a projected loss of habitat caused by the effects of climate change. In order to create effective conservation measures to protect this species, it is important to understand their spatial and temporal distributions. The analysis of passive acoustic monitoring (PAM) data can provide spatial, temporal, and behavioural information through the acoustic detection of a species’ vocalizations. Automated detection methods are necessary to analyze these large volumes of PAM data within realistic time-scales. Deep learning (DL) based methods have recently outperformed more traditional methods for the automated detection of marine mammal vocalizations. This thesis develops the first practical automated ringed seal vocalization detector using DL methods. Specifically, ResNet, a convolutional neural network architecture which has shown success for other marine mammal species, is used to perform binary classification of spectrograms containing ringed seal vocalizations. The ensemble model achieves an F1 score above 0.90 for manually-verified segmented spectrograms in both development environments and those unseen during development. The detector was also deployed on two continuous datasets containing data from different years and/or locations than those seen during development, and achieves greater than 0.90 recall, but reduced precision at approximately 0.50 for both datasets. These results indicate that the detector misses very few ringed seal vocalizations but has a high false positive rate. Many of the false positive detections had similar frequency-domain signatures to ringed seal vocalizations. The detector will be available as an open-source command-line-interface tool for researchers to apply to their own data.