Underwater acoustic target recognition method based on a joint neural network

Han, Xing Cheng; Ren, Chen-Xi; Wei, Liming; Bai, Yunjiao

doi:10.1371/journal.pone.0266425

Cited by 15 publications

(4 citation statements)

References 28 publications

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“…For every screen that qualifies, this R-CNN approach uses algebraic image distortion to determine the inputs of a completely Convolutional with a constant size, regardless of its appearance. The object detection success rate of Quick R-CNN and Rapid R-CNN, both of which are R-CNN based, is significantly higher [12][13][14][15]. The R-CNN model, upon which this Fast R-CNN is built, incorporates the.net programmed features to enhance modelling sensing capabilities and speed up supervised learning.…”

Section: Related Workmentioning

confidence: 99%

Feature Extraction of Underwater Acoustic Signal Target Using Machine Learning Technique

Ashok,

Latha

2024

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Section: Related Workmentioning

confidence: 99%

Feature Extraction of Underwater Acoustic Signal Target Using Machine Learning Technique

Ashok,

Latha

2024

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“…For humans, the sound system includes the auditory centre and the periphery, completes acoustic perception and identification [13]. The auditory system produces audio waves in the following ways: firstly, it absorbs audio waves; next, it sends speed, intensity, and many other data to the hearing centre; & lastly, it integrates & recognizes the data on the auditory system [14][15][16]. The neurological basis of the audio experiment became known through advances in neurobiology.…”

Section: Related Workmentioning

confidence: 99%

Feature Extraction of Underwater Acoustic Signal Target Using Machine Learning Technique

Ashok¹,

Latha²

2022

Preprint

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Due to the complex and interdependent world, underwater acoustic source detection is a difficult challenge. It can be extremely difficult to perform Underwater Acoustic Target Recognition (UATR) utilizing ship-radiated noise due to the complicated aquatic organisms. This paper presents a new deep neural network called Audio Perspective based RCNN (APRCNN), learned by initial underwater acoustic waves with Depth Wise Separable (DWS) Convolutional Deep Neural Network. The APRCNN would then be implemented for underwater acoustic signal prediction and classification. The proposed harvester learning features & integration layers of the SPDNN system were motivated by perceptual processing, and the time-dilated convolution was influenced by machine translation. In this article, the researchers provide a methodology for identifying underwater targets using a thick DNN model. The proposed framework was created to reallocate all previous feature maps to maximize the categorization rate while minimizing the calculation burden in various degraded scenarios. Additionally, the proposed technique allows using the actual audio signal in the transfer function as the right network input information rather than using time-frequency spectrum analyzer images. Based on research findings assessed on a real-world collection of passive sonar, our classification method beats other cutting-edge DNN systems & classic ML methods, achieving an accuracy level of 98.4% at 0-dB SNR.

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“…The recognition of underwater target-radiated noise can be divided into two steps: feature extraction and recognition algorithms. Scholars have been trying for decades to artificially extract features from underwater acoustic targets [ 6 , 7 , 8 ], and the methods include short-time Fourier transform (STFT) [ 9 ], low frequency analysis and recording (LOFAR) [ 10 ], Mel-frequency spectrum [ 6 ], demon noise envelope modulation detection (DEMON) [ 11 ], and Mel Frequency Cepstral Coefficients (MFCC) [ 12 ]. Traditional algorithm, such as GMM [ 13 ] and SVM [ 14 ], were used for the underwater acoustic field.…”

Section: Introductionmentioning

confidence: 99%

A Novel Underwater Acoustic Target Recognition Method Based on MFCC and RACNN

Liu,

Yang,

Hou

et al. 2024

Sensors

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In ocean remote sensing missions, recognizing an underwater acoustic target is a crucial technology for conducting marine biological surveys, ocean explorations, and other scientific activities that take place in water. The complex acoustic propagation characteristics present significant challenges for the recognition of underwater acoustic targets (UATR). Methods such as extracting the DEMON spectrum of a signal and inputting it into an artificial neural network for recognition, and fusing the multidimensional features of a signal for recognition, have been proposed. However, there is still room for improvement in terms of noise immunity, improved computational performance, and reduced reliance on specialized knowledge. In this article, we propose the Residual Attentional Convolutional Neural Network (RACNN), a convolutional neural network that quickly and accurately recognize the type of ship-radiated noise. This network is capable of extracting internal features of Mel Frequency Cepstral Coefficients (MFCC) of the underwater ship-radiated noise. Experimental results demonstrate that the proposed model achieves an overall accuracy of 99.34% on the ShipsEar dataset, surpassing conventional recognition methods and other deep learning models.

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Underwater acoustic target recognition method based on a joint neural network

Cited by 15 publications

References 28 publications

Feature Extraction of Underwater Acoustic Signal Target Using Machine Learning Technique

Feature Extraction of Underwater Acoustic Signal Target Using Machine Learning Technique

Feature Extraction of Underwater Acoustic Signal Target Using Machine Learning Technique

A Novel Underwater Acoustic Target Recognition Method Based on MFCC and RACNN

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