Nonlinear time-warping made simple: a step-by-step tutorial on underwater acoustic modal separation with a single hydrophone

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Bonnel, J., Thode, A., Wright, D., & Chapman, R. Nonlinear time-warping made simple: a step-by-step tutorial on underwater acoustic modal separation with a single hydrophone. The Journal of the Acoustical Society of America, 147(3), (2020): 1897, doi:10.1121/10.0000937.Classical ocean acoustic experiments involve the use of synchronized arrays of sensors. However, the need to cover large areas and/or the use of small robotic platforms has evoked interest in single-hydrophone processing methods for localizing a source or characterizing the propagation environment. One such processing method is “warping,” a non-linear, physics-based signal processing tool dedicated to decomposing multipath features of low-frequency transient signals (frequency f  1 km). Since its introduction to the underwater acoustics community in 2010, warping has been adopted in the ocean acoustics literature, mostly as a pre-processing method for single receiver geoacoustic inversion. Warping also has potential applications in other specialties, including bioacoustics; however, the technique can be daunting to many potential users unfamiliar with its intricacies. Consequently, this tutorial article covers basic warping theory, presents simulation examples, and provides practical experimental strategies. Accompanying supplementary material provides matlab code and simulated and experimental datasets for easy implementation of warping on both impulsive and frequency-modulated signals from both biotic and man-made sources. This combined material should provide interested readers with user-friendly resources for implementing warping methods into their own research.This work was supported by the Office of Naval Research (Task Force Ocean, project N00014-19-1-2627) and by the North Pacific Research Board (project 1810). Original warping developments were supported by the French Delegation Generale de l'Armement

Asymptotic Accuracy of Geoacoustic Inversions

Criteria necessary to accurately estimate a set of unknown geoacoustic parameters from remote acoustic measurements are developed in order to aid the design of geoacoustic experiments. The approach is to have estimation error fall within a specified design threshold by adjusting controllable quantities such as experimental sample size or signal-to-noise ratio (SNR). This is done by computing conditions on sample size and SNR necessary for any estimate to have a variance that (1) asymptotically attains the Cramer–Rao lower bound (CRLB) and (2) has a CRLB that falls within the specified design error threshold. Applications to narrow band deterministic signals received with additive noise by vertical and horizontal arrays in typical continental shelf waveguides are explored. For typical low-frequency scenarios, necessary SNRs and samples sizes can often approach prohibitively large values when a few or more important geoacoustic parameters are unknown, making it difficult to attain practical design thresholds for allowable estimation error

Range-depths tracking of multiple sperm whales over large distances using a two-element vertical array and rhythmic properties of clicks-trains

International audienceSperm whales (Physeter macrocephalus) have followed fishing vessels off the Alaskan coast for decades, in order to remove sablefish ("depredate") from longlines. The Southeast Alaska Sperm Whale Avoidance Project (SEASWAP) has found that whales respond to distinctive acoustic cues made by hauling fishing vessels, as well as to marker buoys on the surface. Between 15-17 August 2010 a simple two-element vertical array was deployed off the continental slope of Southeast Alaska in 1200 m water depth. The array was attached to a longline fishing buoyline at 300 m depth, close to the sound-speed minimum of the deep-water profile. The buoyline also served as a depredation decoy, attracting seven sperm whales to the area. One animal was tagged with both a LIMPET dive depthtransmitting satellite and bioacoustic B-probe tag. Both tag datasets were used as an independent check of a passive acoustic scheme for tracking the whale in depth and range, which exploited the elevation angles and relative arrival times of multiple ray paths recorded on the array. The localization approach doesnt require knowledge of the local bottom bathymetry. Numerical propagation models yielded accurate locations up to at least 35 km range at Beaufort sea state 3. Ongoing work includes combining the arrival angle information with an algorithm developed by Le Bot et al. [1] that uses the rhythmic properties of odontocet click trains to separate interleaved click trains. This approach will improve our localization capabilities in presence of multiple sperm whales. In order to achieve better separation of interleaved click trains it is possible to use machine learning based algorithms. This new concept is based on finding useful information hidden in a large database. This useful information can then be represented by a sparse subspace. The first step of the approach is to extract informative features with a new detector proposed by Dadouchi et al. [2]. Once the dictionary of features is learned, any signal of this considered dataset can be approximated sparsely. By reducing the dimensional space, the sparse representation has the advantage to provide an optimally representation of the data. [Work supported by the North Pacific Research Board, the Alaska SeaLife Center, ONR, NOAA and ANR-12-ASTR-0021-03 "MER CALME"

Accommodating false positives within acoustic spatial capture–recapture, with variable source levels, noisy bearings and an inhomogeneous spatial density

Funding: Tiago Marques was partly supported by CEAUL (funded by FCT - Fundação para a Ciência e a Tecnologia, Portugal, through the project UIDB/00006/2020).Passive acoustic monitoring is a promising method for surveying wildlife populations that are easier to detect acoustically than visually. When animal vocalisations can be uniquely identified on an array of sensors, the potential exists to estimate population density through acoustic spatial capture–recapture (ASCR). However, sound classification is imperfect, and in some situations, a high proportion of sounds detected on just a single sensor (‘singletons’) are not from the target species. We present a case study of bowhead whale calls (Baleana mysticetus) collected in the Beaufort Sea in 2010 containing such false positives. We propose a novel extension of ASCR that is robust to false positives by truncating singletons and conditioning on calls being detected by at least two sensors. We allow for individual-level detection heterogeneity through modelling a variable sound source level, model inhomogeneous call spatial density, and include bearings with varying measurement error. We show via simulation that the method produces near-unbiased estimates when correctly specified. Ignoring source-level variation resulted in a strong negative bias, while ignoring inhomogeneous density resulted in severe positive bias. The case study analysis indicated a band of higher call density approximately 30 km from shore; 59.8% of singletons were estimated to have been false positives.Publisher PDFPeer reviewe

A comparison of three methods for estimating call densities of migrating bowhead whales using passive acoustic monitoring

TAM thanks partial support by Centro de Estatistica e Aplicações, Universidade de Lisboa (funded by FCT—Fundação para a Ciência e a Tecnologia, Portugal, through the project UID/MAT/00006/2013).Various methods for estimating animal density from visual data, including distance sampling (DS) and spatially explicit capture-recapture (SECR), have recently been adapted for estimating call density using passive acoustic monitoring (PAM) data, e.g., recordings of animal calls. Here we summarize three methods available for passive acoustic density estimation: plot sampling, DS, and SECR. The first two require distances from the sensors to calling animals (which are obtained by triangulating calls matched among sensors), but SECR only requires matching (not localizing) calls among sensors. We compare via simulation what biases can arise when assumptions underlying these methods are violated. We use insights gleaned from the simulation to compare the performance of the methods when applied to a case study: bowhead whale call data collected from arrays of directional acoustic sensors at five sites in the Beaufort Sea during the fall migration 2007–2014. Call detections were manually extracted from the recordings by human observers simultaneously scanning spectrograms of recordings from a given site. The large discrepancies between estimates derived using SECR and the other two methods were likely caused primarily by the manual detection procedure leading to non-independent detections among sensors, while errors in estimated distances between detected calls and sensors also contributed to the observed patterns. Our study is among the first to provide a direct comparison of the three methods applied to PAM data and highlights the importance that all assumptions of an analysis method need to be met for correct inference.Publisher PDFPeer reviewe

Using line acceleration to measure false killer whale (Pseudorca crassidens) click and whistle source levels during pelagic longline depredation

False killer whales (Pseudorca crassidens) depredate pelagic longlines in offshore Hawaiian waters. On January 28, 2015 a depredation event was recorded 14m from an integrated GoPro camera, hydrophone, and accelerometer, revealing that false killer whales depredate bait and generate clicks and whistles under good visibility conditions. The act of plucking bait off a hook generated a distinctive 15 Hz line vibration. Two similar line vibrations detected at earlier times permitted the animal’s range and thus signal source levels to be estimated over a 25-min window. Peak power spectral density source levels for whistles (4–8 kHz) were estimated to be between 115 and 130 dB re 1 lPa2/Hz @ 1 m. Echolocation click source levels over 17–32 kHz bandwidth reached 205 dB re 1lPa @ 1 m pk-pk, or 190 dB re 1lPa @ 1 m (root-meansquare). Predicted detection ranges of the most intense whistles are 10 to 25 km at respective sea states of 4 and 1, with click detection ranges being 5 times smaller than whistles. These detection range analyses provide insight into how passive acoustic monitoring might be used to both quantify and avoid depredation encounters.The authors are indebted to Captain Jerry Ray and the rest of the F/V Katy Mary crew for permitting the camera gear to be deployed during their longline fishing trip. Robert Glatts designed the custom GoPro circuit board, and Will Cerf assisted with video footage analysis. This research was sponsored by Derek Orner under the Bycatch Reduction Engineering Program (BREP) at the National Oceanic and Atmospheric Administration (NOAA).Ye

Multi-target 2D tracking method for singing humpback whales using vector sensors

Acoustic vector sensors allow estimating the direction of travel of an acoustic wave at a single point by measuring both acoustic pressure and particle motion on orthogonal axes. In a two-dimensional plane, the location of an acoustic source can thus be determined by triangulation using the estimated azimuths from at least two vector sensors.However, when tracking multiple acoustic sources simultaneously, it becomes challenging to identify and link sequences of azimuthal measurements between sensors to their respective sources. This work illustrates how twodimensional vector sensors, deployed off the coast of western Maui, can be used to generate azimuthal tracks from individual humpback whales singing simultaneously. Incorporating acoustic transport velocity estimates into the processing generates high-quality azimuthal tracks that can be linked between sensors by cross-correlating features of their respective azigrams, a particular time-frequency representation of sound directionality. Once the correct azimuthal track associations have been made between instruments, subsequent localization and tracking in latitude and longitude of simultaneous whales can be achieved using a minimum of two vector sensors. Two-dimensional tracks and positional uncertainties of six singing whales are presented, along with swimming speed estimates derived from a high-quality track

Cues, creaks, and decoys: using passive acoustic monitoring as a tool for studying sperm whale depredation

Since 2003, a collaborative effort (SEASWAP) between fishers, scientists, and managers has researched how Alaskan sperm whales locate demersal longline fishing activity and then depredate sablefish from gear. Sperm whales constantly produce relatively low-frequency biosonar signals whenever foraging; therefore, over the past decade, passive acoustic monitoring (PAM) has become a basic tool, used for both measuring depredation activity and accelerating field tests of potential depredation countermeasures. This paper reviews and summarizes past published PAM research on SEASWAP, and then provides a detailed example of how PAM methods are currently being used to test countermeasures. The review covers two major research thrusts: (i) identifying acoustic outputs of fishing vessels that provide long-distance “cues” that attract whales to fishing activity; and (ii) validating whether distinctive “creak” sounds can be used to quantify and measure depredation rates, using both bioacoustic tags and statistical comparisons between visual and acoustic depredation estimates during federal sablefish surveys. The latter part of the paper then provides an example of how PAM is being used to study a particular potential countermeasure: an “acoustic decoy” which transmits fishing vessel acoustic cues to attract animals away from true fishing activity. The results of an initial 2011 field trial are presented to show how PAM was used to design the decoy signals and monitor the efficacy of the deployment. The ability of PAM to detect both whale presence and depredation behaviour has reduced the need to deploy researchers or other specialists on fishing cruises. Instead, volunteer fishers can deploy “user-friendly” acoustic recorders on their gear, greatly facilitating the testing of various deterrents, and providing the industry and regulators a convenient and unobtrusive tool for monitoring both the scale and long-term spread of this behaviour across the Alaskan fishery