Model-based unsupervised clustering for distinguishing Cuvier's and Gervais' beaked whales in acoustic data

Abstract Passive acoustic monitoring (PAM), particularly autonomous platforms, offers many advantages in monitoring phonating deep-diving marine mammals in oceanic environment. Relevant data can be obtained day and night continuously over long durations and in any weather conditions. It provides a cost-efficient solution with greater detection ranges when compared to traditional large research vessel and aerial visual surveys requiring keeping expert observers on station for long periods of time and relying on good visibility and calm seas. Therefore, PAM is becoming a preferred tool to assess population dynamics trends and health of deep-water marine mammal stocks. However the large volumes of collected data require robust automatic detection and classification algorithms to identify marine mammals in recordings. As for beaked whales, one of the challenging automatic processing goals is the identification of different species to advance our understanding of their role in the marine ecosystem. At present, traditional detection and classification methods employ searches for acoustic events above a user-defined signal-to-noise ratio threshold in the frequency band of interest and further rely on an experienced operator's manual inspection for species classification and removal of false positives. Current passive monitoring data collection systems yield large volumes of acoustic data, therefore a manual classification approach becomes very time-consuming and impractical. This paper focuses on developing a multi-stage automatic classifier for beaked whale species. The proposed method utilizes unsupervised machine clustering of signal attributes extracted from potential detection events flagged by an energy-band detector. The proposed algorithm was benchmarked against a manually annotated workshop dataset and applied to acoustic data collected in the northern Gulf of Mexico. The algorithm classifies beaked whale species in automatic mode with minimal operator involvement only at the validation stage. When compared with the manually annotated classification dataset, the proposed method achieved a recall rate of 82.8% for Cuvier's and 77.9% for Gervais' species in automatic mode. New insights on habitat use by different species of beaked whales in the Gulf of Mexico were gained when using the species-specific classifier. The high spatial resolution acoustic monitoring results showed that the habitat preferences of two dominating beaked whale species in the Gulf of Mexico support the habitat division (ecological niche) hypothesis.