Seabed classification from merchant ship-radiated noise using a physics-based ensemble of deep learning algorithms

Escobar-Amado, Christian D.; Neilsen, Tracianne B.; Castro-Correa, Jhon A.; Komen, David F. Van; Badiey, Mohsen; Knobles, David P.; Hodgkiss, William S.

doi:10.1121/10.0005936

Cited by 15 publications

(2 citation statements)

References 55 publications

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“…Merchant ship-radiated noise was employed for seabed classification using an ensemble of deep learning (DL) algorithms by Escobar-Amado. The accuracy of the five networks was above 97% [29]. A siamese neural network (SNN) was used to detect, classify, and count the calls of four acoustic populations of blue whales, and it outperformed a CNN with a 2% accuracy improvement in population classification [30].…”

Section: Underwater Acoustics Relatedmentioning

confidence: 99%

Underwater Noise Modeling and Its Application in Noise Classification with Small-Sized Samples

et al. 2023

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Underwater noise classification is of great significance for identifying ships as well as other vehicles. Moreover, it is helpful in ensuring a marine habitat-friendly, noise-free ocean environment. But a challenge we are facing is the small-sized underwater noise samples. Because noise is influenced by multiple sources, it is often difficult to determine and label which source or which two sources are dominant. At present, research to solve the problem is focused on noise image processing or advanced computer technology without starting with the noise generation mechanism and modeling. Here, a typical underwater noise generation model (UNGM) is established to augment noise samples. It is established by generating noise with certain kurtosis according to the spectral and statistical characteristics of the actual noise and filter design. In addition, an underwater noise classification model is developed based on UNGM and convolutional neural networks (CNN). Then the UNGM-CNN-based model is used to classify nine types of typical underwater noise, with either the 1/3 octave noise spectrum level (NSL) or power spectral density (PSD) as the input features. The results show that it is effective in improving classification accuracy. Specifically, it increases the classification accuracy by 1.59%, from 98.27% to 99.86%, and by 2.44%, from 97.45% to 99.89%, when the NSL and PSD are used as the input features, respectively. Additionally, the UNGM-CNN-based method appreciably improves macro-precision and macro-recall by approximately 0.87% and 0.83%, respectively, compared to the CNN-based method. These results demonstrate the effectiveness of the UNGM established in noise classification with small-sized samples.

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Section: Underwater Acoustics Relatedmentioning

confidence: 99%

Underwater Noise Modeling and Its Application in Noise Classification with Small-Sized Samples

et al. 2023

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“…With the rapid development of deep learning, it has also yielded promising results in the field of ocean acoustics [4], [5]. Geoacoustic inversion based on deep learning [5], [6] , [7] can be conceptualized as a multi-parameter nonlinear regression problem. The goal of training deep neural networks (DNNs) is for the model to learn the mapping relationship from acoustic signals to geoacoustic parameters, thereby enabling the prediction of geoacoustic parameters based on the input of measured acoustic signals.…”

Section: Introductionmentioning

confidence: 99%

Near-Field Geoacoustic Inversion Using Bottom Reflection Signals via Self-Attention Mechanism

Ma,

Zhang,

Jiang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Geoacoustic inversion typically involves the collection of far-field underwater acoustic data to obtain seabed geoacoustic parameters using empirical formulas and matched field inversion (MFI) techniques. However, acoustic data propagated over long distances can introduce inevitable errors into inversion results, and traditional MFI techniques suffer from low computational efficiency. Although deep learning technologies have been applied to geoacoustic inversion, conventional deep neural network (DNN) models struggle to capture the long and short-term dependencies in bottom reflection data, leading to suboptimal inversion accuracy. These issues present challenges in rapidly and accurately acquiring geoacoustic parameters over large areas. To address this, we propose a near-field bottom reflection signal collection method (NBRC), collecting bottom reflection signals over a wide range of grazing angles by drifting. Utilizing the characteristics of near-field sound propagation, we constructed the Bottom Reflection Coefficient Sequence (BRCS) dataset using the wavenumber integration method. We then introduce a novel deep learning model, Self-Attention Geoacoustics (SAG), based on multi-head self-attention (MHSA) mechanisms, which improves inversion accuracy. Additionally, we propose an adaptive-weight multi-task learning (AW-MTL) training strategy, significantly enhancing the prediction accuracy of sound attenuation. Experimental results demonstrate that our method outperforms conventional geoacoustic inversion methods based on MFI and DNNs in terms of efficiency and accuracy, proving the superiority of our approach.

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Feature extraction methods of ship-radiated noise: From single feature of multi-scale dispersion Lempel-Ziv complexity to mixed double features

Jiang

Tang

et al. 2022

Applied Acoustics

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Seabed classification from merchant ship-radiated noise using a physics-based ensemble of deep learning algorithms

Cited by 15 publications

References 55 publications

Underwater Noise Modeling and Its Application in Noise Classification with Small-Sized Samples

Underwater Noise Modeling and Its Application in Noise Classification with Small-Sized Samples

Near-Field Geoacoustic Inversion Using Bottom Reflection Signals via Self-Attention Mechanism

Feature extraction methods of ship-radiated noise: From single feature of multi-scale dispersion Lempel-Ziv complexity to mixed double features

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