Machine-learning-based simultaneous detection and ranging of impulsive baleen whale vocalizations using a single hydrophone.

The low-frequency impulsive gunshot vocalizations of baleen whales exhibit dispersive propagation in shallow-water channels which is well-modeled by normal mode theory. Typically, underwater acoustic source range estimation requires multiple time-synchronized hydrophone arrays which can be difficult and expensive to achieve. However, single-hydrophone modal dispersion has been used to range baleen whale vocalizations and estimate shallow-water geoacoustic properties. Although convenient when compared to sensor arrays, these algorithms require preliminary signal detection and human labor to estimate the modal dispersion. In this paper, we apply a temporal convolutional network (TCN) to spectrograms from single-hydrophone acoustic data for simultaneous gunshot detection and ranging. The TCN learns ranging and detection jointly using gunshots simulated across multiple environments and ranges along with experimental noise. The synthetic data are informed by only the water column depth, sound speed, and density of the experimental environment, while other parameters span empirically observed bounds. The method is experimentally verified on North Pacific right whale gunshot data collected in the Bering Sea. To do so, 50 dispersive gunshots were manually ranged using the state-of-the-art time-warping inversion method. The TCN detected these gunshots among 50 noise-only examples with high precision and estimated ranges which closely matched those of the physics-based approach.

Classification of dispersive gunshot calls using a convolutional neural network.

A convolutional neural network (CNN) was trained to identify multi-modal gunshots (impulse calls) within large acoustic datasets in shallow-water environments. South Atlantic right whale gunshots were used to train the CNN, and North Pacific right whale (NPRW) gunshots, to which the network was naive, were used for testing. The classifier generalizes to new gunshots from the NPRW and is shown to identify calls which can be used to invert for source range and/or environmental parameters. This can save human analysts hours of manually screening large passive acoustic monitoring datasets.