Automated tracking method improves beaked whale echolocation monitoring
Bridging Echolocation Gaps in Automated Beaked Whale Tracking
Sound
Summary
Tracking beaked whales underwater is hard because they make clicking sounds at irregular times with long pauses. These gaps cause the automated tracking systems to lose track of them frequently. The authors developed a new method that can fill in these gaps by combining advanced tracking with clever ways to connect interrupted clicks. This new approach helps create clearer and longer tracks of the whales, requiring less human effort to analyze the recordings.
What this means in practice
- •For marine biologists: Generate more continuous and accurate tracks of deep-diving whales from acoustic data despite irregular clicking.
- •For oceanographic monitoring teams: Reduce manual labeling workload by automating whale tracking with better handling of click gaps in large audio datasets.
Authors
Clair Ma, Thomas Kropfreiter, Lauren Baggett, Simone Baumann-Pickering, Florian Meyer
Abstract
Passive acoustic monitoring (PAM) is an effective and widely used tool for tracking marine mammals, particularly beaked whales, which are infrequently observed visually because of their deep-diving behavior. However, the large data sets generated by PAM methods often require time-consuming hand labeling to identify whale trajectories in the recorded audio. Automated multi-target tracking (MTT) methods could significantly reduce human workload, but current methods have difficulty forming continuous tracks because of the irregularity of beaked whale echolocation clicks. More precisely, regular sequences of clicks are often interrupted by longer pauses that occur when whales face away from the sensors or stop clicking. Consequently, the probability of detection is difficult to model accurately, and MTT trajectories become fragmented at these pauses. In this paper, we propose a multistage target-estimation method aimed at bridging large gaps in click sequences by combining belief propagation-based MTT with track smoothing and stitching. We validate our method using acoustic recordings of clicks from goose-beaked whales (Ziphius cavirostris), and demonstrate that it improves track estimates and reduces fragmentation in the presence of consecutive missed detections. When evaluated with the generalized optimal subpattern assignment (GOSPA) metric, our method outperforms existing MTT reference methods through reductions in missed-target errors.