Broadband acoustic quantification of mixed biological aggregations at the New England shelf break

Loranger, Scott; Jech, J. Michael; Lavery, Andone C.

doi:10.1121/10.0014910

Cited by 6 publications

(3 citation statements)

References 64 publications

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“…Using narrow (18–38 kHz) and broadband acoustic data (70–280 kHz) with a mixed species scattering model, Loranger et al. (2022) demonstrated estimation of mean length and total biomass of longfin squid and mackerel.…”

Section: Introductionmentioning

confidence: 99%

“…Using the increased range resolution, Hasegawa et al (2021) were able to isolate single fishes and discriminate successfully between average frequency responses of walleye pollock and pointhead flounder in situ . Using narrow and broadband acoustic data (70-280 kHz) with a mixed species scattering model, Loranger et al (2022) demonstrated estimation of mean length and total biomass of longfin squid and mackerel.…”

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confidence: 99%

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Quantitative processing of broadband data as implemented in a scientific split‐beam echosounder

Andersen,

Chu,

Handegard

et al. 2023

Methods Ecol Evol

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The use of quantitative broadband echosounders for biological studies and surveys can offer considerable advantages over narrowband echosounders. These include improved spectral‐based target identification and significantly increased ability to resolve individual targets. An understanding of current processing steps is required to fully utilise and further develop broadband acoustic methods in marine ecology. We describe the steps involved in processing broadband acoustic data from raw data to frequency dependent target strength () and volume backscattering strength () using data from the EK80 broadband scientific echosounder as examples. Although the overall processing steps are described and build on established methods from the literature, multiple choices need to be made during implementation. To highlight and discuss some of these choices and facilitate a common understanding within the community, we have also developed a Python code which will be made publicly available and open source. The code follows the steps using raw data from two single pings, showing the step‐by‐step processing from raw data to and . This code can serve as a reference for developing custom code or implementation in existing processing pipelines, as an educational tool and as a starting point for further development of broadband acoustic methods in fisheries acoustics.

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Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

Quantitative processing of broadband data as implemented in a scientific split‐beam echosounder

Andersen,

Chu,

Handegard

et al. 2023

Methods Ecol Evol

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“…While light can only travel a few hundred meters at most in the ocean before it is absorbed or scattered, acoustic signals can travel on the order of tens, hundreds, or even thousands of kilometers under suitable conditions. It is for this reason that acoustics has been applied to a diverse set of monitoring tasks such as analyzing the physical oceanography of the water column or seafloor properties [1]- [4], monitoring the health of coral reefs [5]- [7], and estimating the density of fish populations and identifying individual species [8]- [11].…”

Section: Chapter 1 Introductionmentioning

confidence: 99%

Automatic baleen whale detection and 2D localization using a network of unsynchonized passive acoustic sensors

Goldwater

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Underwater acoustics is a powerful tool for learning about the ocean’s soniferous marine life. However, most modern acoustic sensing systems consist of expensive arrays of timesynchronized recorders which require a crewed research vessel and significant expertise to deploy, operate, and recover. Recently, there has been a growing corpus of research related to algorithms for low-cost and accessible acoustic hardware. Deep learning methods have shown great promise when applied to underwater acoustics inverse problems. While many signal processing or physics-based algorithms exhibit long run times and require manual labor to extract signals of interest, tune parameters, as well as visually verify the results, an appropriately trained neural network can quickly process data with no human supervision. Both low-cost passive acoustic monitoring (PAM) sensing platforms and algorithms that can analyze massive amounts of raw data are critical to accessible and scalable approaches in ocean acoustic monitoring. This thesis presents a method for detection and 2D (latitude-longitude) localization of underwater acoustic sources without requiring synchronized sensors. The signals of interest here are the dispersive low-frequency impulsive gunshot vocalizations of North Pacific and North Atlantic right whales (NPRWs, NARWs). In shallow-water channels, the timefrequency representation of the received signal is strongly dependent on source-receiver range, making these impulses ideal candidates for range-based localization. The first step in the localization pipeline uses a temporal convolutional network (TCN) to simultaneously detect gunshot vocalizations and predict their ranges. Trained on spectrograms of synthetic data simulated in a variety of environments, the TCN is applied to PAM data from moorings in the Bering Sea. Gunshots are detected with high precision, and the range estimates are comparable to those estimated using traditional physics-based processing. Both methods use a minimal set of a priori environmental information including water column depth, sound speed, and density. Depending on the sensor layout, the TCN may need to scan large windows of data, so the number of unique acoustic sources is unknown. To automatically associate and localize range measurements, the proposed method seeks subsets of measurements across unique sensors which are internally consistent. For every considered measurement subset, locations are estimated with single constituent measurements left out and checked to be sufficiently close to the excluded measurement’s set of potential locations. If a measurement subset is entirely consistent in this manner, the measurements are added as neighboring nodes in a graph-based representation, and strongly connected components are used to determine data associations and calculate the final source location estimates. Informed by the methods developed in this thesis, an array of low-cost TOSSIT moorings was deployed in Cape Cod Bay and used to collect experimental PAM data. The localization results are comparable to another similar physics-based inversion approach. Overall, this thesis aims to fill a gap in acoustic data processing methods where data from a low-cost network of unsynchronized acoustic sensors are fused to localize acoustic sources. The presented methods and data processing pipeline demonstrate the great potential of low-cost acoustic sensing systems.

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A Bayesian inverse approach to identify and quantify organisms from fisheries acoustic data

Urmy

Robertis

Bassett

2023

ICES Journal of Marine Science

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Identifying sound-scattering organisms is a perennial challenge in fisheries acoustics. Most practitioners classify backscatter based on direct sampling, frequency-difference thresholds, and expert judgement, then echo-integrate at a single frequency. However, this approach struggles with species mixtures, and discards multi-frequency information when integrating. Inversion methods do not have these limitations, but are not widely used because species identifications are often ambiguous and the algorithms are complicated to implement. We address these shortcomings using a probabilistic, Bayesian inversion method. Like other inversion methods, it handles species mixtures, uses all available frequencies, and extends naturally to broadband signals. Unlike previous approaches, it leverages Bayesian priors to rigorously incorporate information from direct sampling and biological knowledge, constraining the inversion and reducing ambiguity in species identification. Because it is probabilistic, a well-specified model should not produce solutions that are both wrong and confident. The model is based on physical scattering processes, so its output is fully interpretable, unlike some machine learning methods. Finally, the approach can be implemented using existing Bayesian libraries and is easily parallelized for large datasets. We present examples using simulations and field data from the Gulf of Alaska, and discuss possible applications and extensions of the method.

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Broadband acoustic quantification of mixed biological aggregations at the New England shelf break

Cited by 6 publications

References 64 publications

Quantitative processing of broadband data as implemented in a scientific split‐beam echosounder

Quantitative processing of broadband data as implemented in a scientific split‐beam echosounder

Automatic baleen whale detection and 2D localization using a network of unsynchonized passive acoustic sensors

A Bayesian inverse approach to identify and quantify organisms from fisheries acoustic data

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