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Chenhong Yan, Shefeng Yan, Tianyi Yao, Yang Yu, Guang Pan, Lu Liu, Mou Wang and Jisheng Bai
Ship-radiated noise classification is critical in ocean acoustics. Recently, the feature extraction method combined with time?frequency spectrograms and convolutional neural networks (CNNs) has effectively described the differences between various underw...
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Shuang Yang, Lingzhi Xue, Xi Hong and Xiangyang Zeng
Recently, deep learning has been widely used in ship-radiated noise classification. To improve classification efficiency, avoiding high computational costs is an important research direction in ship-radiated noise classification. We propose a lightweight...
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Yuxing Li, Yilan Lou, Lili Liang and Shuai Zhang
In recent years, fuzzy dispersion entropy (FDE) has been proposed and used in the feature extraction of various types of signals. However, FDE can only analyze a signal from a single time scale during practical application and ignores some important info...
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Nicholas Marasco, Haidy Elghamrawy and Donald McGaughey
The current state of the art in hydroacoustics research employs a variety of feature extraction techniques with the goal of accurately classifying a ship based on its radiated noise. These techniques are capable of accuracy in excess of 95%. A question a...
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Xinwei Luo, Minghong Zhang, Ting Liu, Ming Huang and Xiaogang Xu
This paper focuses on the automatic target recognition (ATR) method based on ship-radiated noise and proposes an underwater acoustic target recognition (UATR) method based on ResNet. In the proposed method, a multi-window spectral analysis (MWSA) method ...
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