Observed transmissions and ocean-ice-acoustic coupled modelling in the Beaufort Sea.

An ocean-ice-acoustic coupled model is configured for the Beaufort Sea. The model uses outputs from a data assimilating global scale ice-ocean-atmosphere forecast to drive a bimodal roughness algorithm for generating a realistic ice canopy. The resulting range-dependent ice cover obeys observed roughness, keel number density, depth, and slope, and floe size statistics. The ice is inserted into a parabolic equation acoustic propagation model as a near-zero impedance fluid layer along with a model defined range-dependent sound speed profile. Year-long observations of transmissions at 35 Hz from the Coordinated Arctic Acoustic Thermometry Experiment and 925 Hz from the Arctic Mobile Observing System source were recorded over the winter of 2019-2020 on a free-drifting, eight-element vertical line array designed to vertically span the Beaufort duct. The ocean-ice-acoustic coupled model predicts receive levels that reasonably agree with the measurements over propagation ranges of 30-800 km. At 925 Hz, seasonal and sub-seasonal ocean and ice driven variations of propagation loss are captured in the data and reproduced in the model.

Range-dependent impacts of ocean acoustic propagation on automated classification of transmitted bowhead and humpback whale vocalizations.

Significant effort has been made over the last few decades to develop automated passive acoustic monitoring (PAM) systems capable of classifying cetaceans at the species level. The utility of such systems depends on the systems' ability to operate across a wide range of ocean acoustic environments; however, anecdotal evidence suggests that site-specific propagation characteristics impact the performance of PAM systems. Variability in propagation characteristics leads to differences in how each cetacean vocalization is altered as it propagates along the source-receiver path. A propagation experiment was conducted in the Gulf of Mexico to investigate the range-dependent impacts of acoustic propagation on the performance of an automated classifier. Modified bowhead and humpback vocalizations were transmitted over ranges from 1 to 10 km. When the classifier was trained with signals collected near the sound source, it was found that the performance decreased with increasing transmission range-this appeared to be largely explained by decreasing signal-to-noise ratio (SNR). Generation of performance matrices showed that one method to develop a classifier that maintains high performance across many ranges is to include a varied assortment of ranges in the training data; however, if the training set is limited, it is best to train on relatively low SNR vocalizations.

Examining the robustness of automated aural classification of active sonar echoes.

Active sonar systems are used to detect underwater man-made objects of interest (targets) that are too quiet to be reliably detected with passive sonar. Performance of active sonar can be degraded by false alarms caused by echoes returned from geological seabed structures (clutter) in shallow regions. To reduce false alarms, a method of distinguishing target echoes from clutter echoes is required. Research has demonstrated that perceptual-based signal features similar to those employed in the human auditory system can be used to automatically discriminate between target and clutter echoes, thereby reducing the number of false alarms and improving sonar performance. An active sonar experiment on the Malta Plateau in the Mediterranean Sea was conducted during the Clutter07 sea trial and repeated during the Clutter09 sea trial. The dataset consists of more than 95,000 pulse-compressed echoes returned from two targets and many geological clutter objects. These echoes were processed using an automatic classifier that quantifies the timbre of each echo using a number of perceptual signal features. Using echoes from 2007, the aural classifier was trained to establish a boundary between targets and clutter in the feature space. Temporal robustness was then investigated by testing the classifier on echoes from the 2009 experiment.

Automated aural classification used for inter-species discrimination of cetaceans.

Passive acoustic methods are in widespread use to detect and classify cetacean species; however, passive acoustic systems often suffer from large false detection rates resulting from numerous transient sources. To reduce the acoustic analyst workload, automatic recognition methods may be implemented in a two-stage process. First, a general automatic detector is implemented that produces many detections to ensure cetacean presence is noted. Then an automatic classifier is used to significantly reduce the number of false detections and classify the cetacean species. This process requires development of a robust classifier capable of performing inter-species classification. Because human analysts can aurally discriminate species, an automated aural classifier that uses perceptual signal features was tested on a cetacean data set. The classifier successfully discriminated between four species of cetaceans-bowhead, humpback, North Atlantic right, and sperm whales-with 85% accuracy. It also performed well (100% accuracy) for discriminating sperm whale clicks from right whale gunshots. An accuracy of 92% and area under the receiver operating characteristic curve of 0.97 were obtained for the relatively challenging bowhead and humpback recognition case. These results demonstrated that the perceptual features employed by the aural classifier provided powerful discrimination cues for inter-species classification of cetaceans.

Performances of human listeners and an automatic aural classifier in discriminating between sonar target echoes and clutter.

Human listening tests were conducted to investigate if participants could distinguish between samples of target echoes and clutter obtained from a broadband active sonar experiment. For each echo, the listeners assigned a rating based on how confident they were that it was a target echo or clutter. The measure of performance was the area under the binormal receiver-operating-characteristic (ROC) curve, A(z). The mean performance was A(z)=0.95 ± 0.04 when signals were presented with their full available acoustic bandwidth of approximately 0-2 kHz. It was A(z)=0.77 ± 0.08 when the bandwidth was reduced to 0.5-2 kHz. The error bounds are stated as 95% confidence intervals. These results show that the listeners could definitely hear differences, but their performance was significantly degraded when the low-frequency signal information was removed. The performance of an automatic aural classifier was compared against this human-performance baseline. Results of statistical tests showed that it outperformed 2 of 13 listeners and 5 of 9 human listeners in the full-bandwidth and reduced-bandwidth tests, respectively, and performed similarly to the other listeners. Given its performance, the automatic aural classifier may prove beneficial to Navy sonar systems.

A lexical basis for N400 context effects: evidence from MEG.

The electrophysiological response to words during the 'N400' time window (approximately 300-500 ms post-onset) is affected by the context in which the word is presented, but whether this effect reflects the impact of context on access of the stored lexical information itself or, alternatively, post-access integration processes is still an open question with substantive theoretical consequences. One challenge for integration accounts is that contexts that seem to require different levels of integration for incoming words (i.e., sentence frames vs. prime words) have similar effects on the N400 component measured in ERP. In this study we compare the effects of these different context types directly, in a within-subject design using MEG, which provides a better opportunity for identifying topographical differences between electrophysiological components, due to the minimal spatial distortion of the MEG signal. We find a qualitatively similar contextual effect for both sentence frame and prime-word contexts, although the effect is smaller in magnitude for shorter word prime contexts. Additionally, we observe no difference in response amplitude between sentence endings that are explicitly incongruent and target words that are simply part of an unrelated pair. These results suggest that the N400 effect does not reflect semantic integration difficulty. Rather, the data are consistent with an account in which N400 reduction reflects facilitated access of lexical information.

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.

Perception-based automatic classification of impulsive-source active sonar echoes.

Impulsive-source active sonar systems are often plagued by false alarm echoes resulting from the presence of naturally occurring clutter objects in the environment. Sonar performance could be improved by a technique for discriminating between echoes from true targets and echoes from clutter. Motivated by anecdotal evidence that target echoes sound very different than clutter echoes when auditioned by a human operator, this paper describes the implementation of an automatic classifier for impulsive-source active sonar echoes that is based on perceptual signal features that have been previously identified in the musical acoustics literature as underlying timbre. Perceptual signal features found in this paper to be particularly useful to the problem of active sonar classification include: the centroid and peak value of the perceptual loudness function, as well as several features based on subband attack and decay times. This paper uses subsets of these perceptual signal features to train and test an automatic classifier capable of discriminating between target and clutter echoes with an equal error rate of roughly 10%; the area under the receiver operating characteristic curve corresponding to this classifier is found to be 0.975.

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.

Acoustic backscatter measurements from littoral seabeds at shallow grazing angles at 4 and 8 kHz.

Direct measurement of acoustic scattering from the seabed at shallow grazing angles and low kilohertz frequencies presents a considerable challenge in littoral waters. Specifically, returns from the air-water interface typically contaminate the signals of interest. To address this issue, DRDC Atlantic has developed a sea-going research system for measuring acoustic scatter from the seabed in shallow-water environs. The system, known as the wideband sonar (WBS), consists of a parametric array transmitter and a superdirective receiver. In this paper, backscatter measurements obtained with the WBS at two sandy, shallow-water sites off North America's Atlantic coast are presented. Data were collected at 4 and 8 kHz at grazing angles from 3 degrees-15 degrees. The backscattering strength is similar at both sites and, below about 8 degrees, it appears to be independent of frequency within the statistical accuracy of the data. The measurements show reasonable agreement with model estimates of backscatter from sandy sediments. A small data set was collected at one of the sites to examine the feasibility of using the WBS to measure the azimuthal variability of acoustic scatter. The data set--although limited--indicates that the parametric array's narrow beamwidth makes the system well-suited to this task.