A machine learning pipeline for classification of cetacean echolocation clicks in large underwater acoustic datasets
Fig 2
Modeled delphinid echolocation click received level distributions (solid black line) predict an exponential increase in the number of clicks detected (approximately linear in log space shown here) as received levels decline, assuming animals are uniformly distributed on average around a stationary sensor [38,39]. This shape is driven by an inverse relationship between range and received level (although signal directionality and other factors can introduce additional variation) and by the area monitored, which increases with the square of the monitoring radius, leading to greater numbers of animals at large ranges. Circles illustrate a typical “real-world” received level distribution from a click detector, in which detections approaching the intended threshold (115 dBPP re 1μPa in this case) begin to be systematically missed. Enforcing a higher minimum amplitude threshold at which detection counts are still increasing (e.g. dashed line at 120 dBPP re 1μPa) greatly simplifies subsequent analyses such as species-specific missed-rates and density estimates. More information on the model used in this illustration is available in [38].