Abstract:Acoustics is the primary means of long-range and wide-area sensing in the ocean due to the severe attenuation of electromagnetic waves in seawater. While it is known that densely packed fish groups can attenuate acoustic signals during long-range propagation in an ocean waveguide, previous experimental demonstrations have been restricted to single line transect measurements of either transmission or backscatter and have not directly investigated wide-area sensing and communication issues. Here we experimentall… Show more
“…The underwater recordings of fin whale vocalizations analyzed here are drawn from the NorEx14, conducted by a collaborative team from the Massachusetts Institute of Technology, Northeastern University, NOAA-Northeast Fisheries Science Center, Naval Research Laboratory, Penn State University and the Woods Hole Oceanographic Institution in the United States, as well as the Institute of Marine Research-Bergen (IMR) in Norway. The NorEx14 was conducted from 18 February to 8 March 2014, in conjunction with the IMR survey of spawning populations of Atlantic herring off the Alesund coast, the Atlantic cod off the Lofoten peninsula, and the capelin off the Northern Finnmark region [33,34]. The twofold objectives of the NorEx14 were to (i) image and monitor the population distributions of these large fish shoals from diverse species instantaneously over wide areas of their spawning grounds, using the Ocean Acoustic Waveguide Remote Sensing (OAWRS) and imaging system [33,[35][36][37] from which fish group behavioral patterns can be quantified, and (ii) observe marine mammal vocalizations and infer their temporal-spatial distributions over wide areas using the POAWRS technique, [1,2,9,11] combined with visual observations for species confirmation.…”
Section: Measurement Of Fin Whale Vocalizations Using a Coherent Hydrmentioning
A large variety of sound sources in the ocean, including biological, geophysical, and man-made, can be simultaneously monitored over instantaneous continental-shelf scale regions via the passive ocean acoustic waveguide remote sensing (POAWRS) technique by employing a large-aperture densely-populated coherent hydrophone array system. Millions of acoustic signals received on the POAWRS system per day can make it challenging to identify individual sound sources. An automated classification system is necessary to enable sound sources to be recognized. Here, the objectives are to (i) gather a large training and test data set of fin whale vocalization and other acoustic signal detections; (ii) build multiple fin whale vocalization classifiers, including a logistic regression, support vector machine (SVM), decision tree, convolutional neural network (CNN), and long short-term memory (LSTM) network; (iii) evaluate and compare performance of these classifiers using multiple metrics including accuracy, precision, recall and F1-score; and (iv) integrate one of the classifiers into the existing POAWRS array and signal processing software. The findings presented here will (1) provide an automatic classifier for near real-time fin whale vocalization detection and recognition, useful in marine mammal monitoring applications; and (2) lay the foundation for building an automatic classifier applied for near real-time detection and recognition of a wide variety of biological, geophysical, and man-made sound sources typically detected by the POAWRS system in the ocean.
“…The underwater recordings of fin whale vocalizations analyzed here are drawn from the NorEx14, conducted by a collaborative team from the Massachusetts Institute of Technology, Northeastern University, NOAA-Northeast Fisheries Science Center, Naval Research Laboratory, Penn State University and the Woods Hole Oceanographic Institution in the United States, as well as the Institute of Marine Research-Bergen (IMR) in Norway. The NorEx14 was conducted from 18 February to 8 March 2014, in conjunction with the IMR survey of spawning populations of Atlantic herring off the Alesund coast, the Atlantic cod off the Lofoten peninsula, and the capelin off the Northern Finnmark region [33,34]. The twofold objectives of the NorEx14 were to (i) image and monitor the population distributions of these large fish shoals from diverse species instantaneously over wide areas of their spawning grounds, using the Ocean Acoustic Waveguide Remote Sensing (OAWRS) and imaging system [33,[35][36][37] from which fish group behavioral patterns can be quantified, and (ii) observe marine mammal vocalizations and infer their temporal-spatial distributions over wide areas using the POAWRS technique, [1,2,9,11] combined with visual observations for species confirmation.…”
Section: Measurement Of Fin Whale Vocalizations Using a Coherent Hydrmentioning
A large variety of sound sources in the ocean, including biological, geophysical, and man-made, can be simultaneously monitored over instantaneous continental-shelf scale regions via the passive ocean acoustic waveguide remote sensing (POAWRS) technique by employing a large-aperture densely-populated coherent hydrophone array system. Millions of acoustic signals received on the POAWRS system per day can make it challenging to identify individual sound sources. An automated classification system is necessary to enable sound sources to be recognized. Here, the objectives are to (i) gather a large training and test data set of fin whale vocalization and other acoustic signal detections; (ii) build multiple fin whale vocalization classifiers, including a logistic regression, support vector machine (SVM), decision tree, convolutional neural network (CNN), and long short-term memory (LSTM) network; (iii) evaluate and compare performance of these classifiers using multiple metrics including accuracy, precision, recall and F1-score; and (iv) integrate one of the classifiers into the existing POAWRS array and signal processing software. The findings presented here will (1) provide an automatic classifier for near real-time fin whale vocalization detection and recognition, useful in marine mammal monitoring applications; and (2) lay the foundation for building an automatic classifier applied for near real-time detection and recognition of a wide variety of biological, geophysical, and man-made sound sources typically detected by the POAWRS system in the ocean.
“…Acoustic recordings of ship-radiated underwater sound were received on a large-aperture densely-sampled coherent hydrophone array containing 160-elements deployed during the Nordic Seas 2014 Experiment (NorEx2014) [31][32][33][34][35][36]. The three approaches for analyzing different aspects of a ship's radiated underwater sound are applied to beamformed pressure-time series data spanning 360 • horizontal azimuths about the coherent hydrophone array.…”
Three approaches for instantaneous wide-area analysis of ship-radiated underwater sound, each focusing on a different aspect of that sound, received on a large-aperture densely-sampled coherent hydrophone array have been developed. (i) Ship’s narrowband machinery tonal sound is analyzed via temporal coherence using Mean Magnitude-Squared Coherence (MMSC) calculations. (ii) Ship’s broadband amplitude-modulated cavitation noise is examined using Cyclic Spectral Coherence (CSC) analysis that provides estimates for propeller blade pass rotation frequency, shaft rotation frequency, and hence the number of propeller blades. (iii) Mean power spectral densities (PSD) averaged across broad bandwidths are calculated in order to detect acoustically energetic ships. Each of these techniques are applied after beamforming of the received acoustic signals on a coherent hydrophone array, leading to significantly enhanced signal-to-noise ratios for simultaneous detection, bearing-time estimation and acoustic signature characterization of multiple ships over continental-shelf scale regions. The approaches are illustrated with underwater recordings of a 160-element coherent hydrophone array for six ocean vessels, that are located at a variety of bearings and ranges out to 200 km from the array, in the Norwegian Sea in February 2014. The CSC approach is shown to also be useful for automatic detection and bearing-time estimation of repetitive marine mammal vocalizations, providing estimates for inter-pulse-train and inter-pulse intervals from CSC spectra cyclic fundamental and first recurring peak frequencies respectively.
“…The range-dependent decay in OAWRS transmissions is corrected with a theoretical formulation that has been previously shown to be consistent with experimental measurements of attenuation from fish in a waveguide environment [43,15]. After applying the attenuation correction, wide-area population density maps can be generated even during dense shoaling activity.…”
Section: Introductionmentioning
confidence: 92%
“…OAWRS images are corrected for attenuation from fish scattering using a theoretical formulation that has been previously shown to be consistent with experimental measurements of attenuation from fish in a waveguide [43,15]. The theoretical decay due to fish attenuation depends on the average population density of fish within the sensing region, which is determined for each OAWRS transmission by modeling scattering strength uncorrected for losses from attenuation ("fish-attenuated scattering strength") and performing a leastsquares fit with measurements (Figure 3-4).…”
Section: Correcting For Attenuation From Herring In Wide-area Oawrs Population Density Mapsmentioning
confidence: 99%
“…Measured sensing range for the OAWRS system in the presence of Ålesund herring and Finnmark capelin (red dots in A and B) are shown to be in agreement with expected sensing range at the relevant 𝑊 0 − 𝑁 𝐿 0 values (red lines in A and B). Physical parameters used for modeling sensing range for Finnmark capelin and Lofoten cod are shown in Table 1 of [15]. Sensing range predictions for Lofoten cod exceed the maximum possible range that could be recorded in the 50 second recording window that was used in this region (red dot in C).…”
Section: Spatial Undersampling In Echosounder Surveysmentioning
Attenuation from fish can reduce the intensity of acoustic signals and significantly decrease detection range for long-range active and passive sensing in the ocean. This makes it important to understand the relevant mechanisms and accurately predict attenuation from fish in underwater acoustic sensing. Formulations for predicting attenuation from fish, however, depend on the accurate characterization of population density and spatial distribution of fish groups along long-range propagation paths, which is difficult to achieve using conventional survey methods. In previous investigations of attenuation from fish, population densities were inferred from reductions in the intensity of long-range acoustic signals caused by diel or seasonal shoaling patterns of fish groups. Here, Ocean Acoustic Waveguide Remote Sensing (OAWRS) is used to instantaneously image massive Norwegian herring shoals that stretch for thousands of square kilometers and simultaneously measure attenuation from these shoals within the active OAWRS transmissions, as well as attenuation to ship-radiated tonals detected by Passive Ocean Acoustic Waveguide Remote Sensing (POAWRS). Reductions in signal intensity are predicted using a normal-mode-based analytical theory derived from first principles for acoustic propagation and scattering through inhomogeneities in an ocean waveguide. The predictions of the waveguide attenuation formulation are in agreement with measured reductions from attenuation, where the position, size, and population density of the fish groups are characterized using OAWRS imagery as well as in situ echosounder measurements of the specific shoals occluding the propagation path. Common heuristic formulations that employ free space scattering assumptions for attenuation from fish groups are not in agreement with measurements here, and waveguide scattering theory is found to be necessary for accurate predictions. It is experimentally and theoretically shown that attenuation can be significant when the sensing frequency is near the resonance frequency of the shoaling fish, where scattering losses from the fish swimbladders and damping from fish flesh is most significant. Negligible attenuation was observed in previous OAWRS and POAWRS surveys because the frequency of the acoustic signals was sufficiently far from the swimbladder resonance peak of the shoaling fish or the packing densities of the fish shoals were not sufficiently high.
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