Discriminative Ensemble Loss for Deep Neural Network on Classification of Ship-Radiated Noise

He, Lei; Shen, Xiaohong; Zhang, Muhang; Wang, Haiyan

doi:10.1109/lsp.2021.3057539

Cited by 5 publications

(2 citation statements)

References 33 publications

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“…However, the mechanism of radiated noise generated by underwater targets is very complex, and the radiated noise contains multiple components, including continuous spectrum components and strong discrete spectrums. Furthermore, many factor such as spatiotemporal variations in the underwater acoustic channel, multipath effects, and Doppler effects can all affect the propagation of underwater acoustic signals [ 3 , 4 , 5 ]. As a result, UATR is a highly challenging and difficult technology.…”

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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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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“…Beyond the architectural design, reducing the need of real-world data for deep neural networks in ship classification tasks by improving learning strategies has also attracted much attention. [19] argued that the unsupervised pre-training can enable the deep long short-term memory network to effectively address the lack of data; by augmenting the 3D mel-spectrogram of ship-radiated noise in the time and frequency domains [20], it was believed that the classification performance can be improved with limited real-world data; and [21] considered that the performance of ship classification tasks would benefit from the ensemble of conventional SoftMax loss and metric-based loss when optimizing the models. However, the available training samples in existing works on classification of ship-radiated noises were still more than hours even with limited real-world data, and the situation of only a few available samples (e.g., <10 spectrograms for each vessel) that might be faced in ship identification has not been fully discussed.…”

Section: Introductionmentioning

confidence: 99%

A Contrastive-Learning-Based Method for the Few-Shot Identification of Ship-Radiated Noises

Nie

Wang

et al. 2023

JMSE

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For identifying each vessel from ship-radiated noises with only a very limited number of data samples available, an approach based on the contrastive learning was proposed. The input was sample pairs in the training, and the parameters of the models were optimized by maximizing the similarity of sample pairs from the same vessel and minimizing that from different vessels. In practical inference, the method calculated the distance between the features of testing samples and those of registration templates and assigned the testing sample into the closest templates for it to achieve the parameter-free classification. Experimental results on different sea-trial data demonstrated the advantages of the proposed method. On the five-ship identification task based on the open-source data, the proposed method achieved an accuracy of 0.68 when only five samples per vessel were available, that was significantly higher than conventional solutions with accuracies of 0.26 and 0.48. Furthermore, the convergence of the method and the behavior of its performance with increasing data samples available for the training were discussed empirically.

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DSCANet: underwater acoustic target classification using the depthwise separable convolutional attention module

Tang,

2024

Earth Sci Inform

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Discriminative Ensemble Loss for Deep Neural Network on Classification of Ship-Radiated Noise

Cited by 5 publications

References 33 publications

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

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

A Contrastive-Learning-Based Method for the Few-Shot Identification of Ship-Radiated Noises

DSCANet: underwater acoustic target classification using the depthwise separable convolutional attention module

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