Automatic detection and classification of bearded seal vocalizations in the northeastern Chukchi Sea using convolutional neural networks.

Bearded seals vocalizations are often analyzed manually or by using automatic detections that are manually validated. In this work, an automatic detection and classification system (DCS) based on convolutional neural networks (CNNs) is proposed. Bearded seal sounds were year-round recorded by four spatially separated receivers on the Chukchi Continental Slope in Alaska in 2016-2017. The DCS is divided in two sections. First, regions of interest (ROI) containing possible bearded seal vocalizations are found by using the two-dimensional normalized cross correlation of the measured spectrogram and a representative template of two main calls of interest. Second, CNNs are used to validate and classify the ROIs among several possible classes. The CNNs are trained on 80% of the ROIs manually labeled from one of the four spatially separated recorders. When validating on the remaining 20%, the CNNs show an accuracy above 95.5%. To assess the generalization performance of the networks, the CNNs are tested on the remaining recorders, located at different positions, with a precision above 89.2% for the main class of the two types of calls. The proposed technique reduces the laborious task of manual inspection prone to inconstant bias and possible errors in detections.

Temporal and spatial dependence of a yearlong record of sound propagation from the Canada Basin to the Chukchi Shelf.

The Pacific Arctic Region has experienced decadal changes in atmospheric conditions, seasonal sea-ice coverage, and thermohaline structure that have consequences for underwater sound propagation. To better understand Arctic acoustics, a set of experiments known as the deep-water Canada Basin acoustic propagation experiment and the shallow-water Canada Basin acoustic propagation experiment was conducted in the Canada Basin and on the Chukchi Shelf from summer 2016 to summer 2017. During the experiments, low-frequency signals from five tomographic sources located in the deep basin were recorded by an array of hydrophones located on the shelf. Over the course of the yearlong experiment, the surface conditions transitioned from completely open water to fully ice-covered. The propagation conditions in the deep basin were dominated by a subsurface duct; however, over the slope and shelf, the duct was seen to significantly weaken during the winter and spring. The combination of these surface and subsurface conditions led to changes in the received level of the sources that exceeded 60 dB and showed a distinct spacio-temporal dependence, which was correlated with the locations of the sources in the basin. This paper seeks to quantify the observed variability in the received signals through propagation modeling using spatially sparse environmental measurements.

Azimuthal and temporal sound fluctuations on the Chukchi continental shelf during the Canada Basin Acoustic Propagation Experiment 2017.

The Shallow Water Canada Basin Acoustic Propagation Experiment was conducted on the Chukchi Sea continental shelf from October 2016 to November 2017. The experimental goals were to access (1) long-range (basin-scale) and (2) short-range (shallow-water) spatial and temporal energy variation. This letter focuses on a 20-dB energy change of acoustic signals in the frequency band 700-1100 Hz from June to August 2017 occurring along two shallow-water tracks from a common source, correlated with the occurrence of an oceanographic event in the top 150-m water column due to a Pacific Water outflow from the Bering Sea and retreat of the Marginal Ice Zone.

Transmission loss measurements and geoacoustic sensitivity modeling at 1.2 kHz.

The Shallow Water Experiment 2006 was conducted off the coast of New Jersey in the summer of 2006. Defence Research and Development Canada-Atlantic performed a series of experiments designed to validate the use of rapid environmental assessment tools and methods to improve active sonar performance predictions. The sensitivity of acoustic propagation to a varying or uncertain environment is determined by examining the relative change of acoustic pressure caused by environmental variability, using the method described recently [Dosso et al., J. Acoust. Soc. Am. 121, 42 (2007)]. The variability of the modeled environmental parameters is based on measured and estimated oceanographic and geoacoustic properties. The resulting sensitivity is compared to measured transmission loss data at 1.2 kHz.

Spatial field shifts in ocean acoustic environmental sensitivity analysis.

This article examines the effects of spatial field shifts in ocean acoustic environmental sensitivity analysis. Acoustic sensitivity studies are typically based on comparing acoustic fields computed for a reference environmental model and for a perturbed model in which one or more parameters have been changed. The perturbation to the acoustic field due to the perturbed environment generally includes a component representing a spatial shift of the field (i.e., local field structure remains coherent, but shifts in range and/or depth) and a component representing a change to the shifted field. Standard sensitivity measures based on acoustic perturbations at a fixed point can indicate high sensitivity in cases where the field structure changes very little, but is simply shifted by a small spatial offset; this can conflict with an intuitive understanding of sensitivity. This article defines and compares fixed-point and field-shift corrected sensitivity measures. The approaches are illustrated with examples of deterministic sensitivity (i.e., sensitivity to a specific environmental change) and stochastic sensitivity (sensitivity to environmental uncertainty) in range-independent and range-dependent environments.