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Open AccessArticle

Ship Type Classification by Convolutional Neural Networks with Auditory-Like Mechanisms

School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
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Author to whom correspondence should be addressed.
Sensors 2020, 20(1), 253; https://doi.org/10.3390/s20010253
Received: 1 November 2019 / Revised: 17 December 2019 / Accepted: 26 December 2019 / Published: 1 January 2020
Ship type classification with radiated noise helps monitor the noise of shipping around the hydrophone deployment site. This paper introduces a convolutional neural network with several auditory-like mechanisms for ship type classification. The proposed model mainly includes a cochlea model and an auditory center model. In cochlea model, acoustic signal decomposition at basement membrane is implemented by time convolutional layer with auditory filters and dilated convolutions. The transformation of neural patterns at hair cells is modeled by a time frequency conversion layer to extract auditory features. In the auditory center model, auditory features are first selectively emphasized in a supervised manner. Then, spectro-temporal patterns are extracted by deep architecture with multistage auditory mechanisms. The whole model is optimized with an objective function of ship type classification to form the plasticity of the auditory system. The contributions compared with an auditory inspired convolutional neural network include the improvements in dilated convolutions, deep architecture and target layer. The proposed model can extract auditory features from a raw hydrophone signal and identify types of ships under different working conditions. The model achieved a classification accuracy of 87.2% on four ship types and ocean background noise. View Full-Text
Keywords: machine learning; neural network; ship radiated noise; underwater acoustics machine learning; neural network; ship radiated noise; underwater acoustics
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MDPI and ACS Style

Shen, S.; Yang, H.; Yao, X.; Li, J.; Xu, G.; Sheng, M. Ship Type Classification by Convolutional Neural Networks with Auditory-Like Mechanisms. Sensors 2020, 20, 253.

AMA Style

Shen S, Yang H, Yao X, Li J, Xu G, Sheng M. Ship Type Classification by Convolutional Neural Networks with Auditory-Like Mechanisms. Sensors. 2020; 20(1):253.

Chicago/Turabian Style

Shen, Sheng; Yang, Honghui; Yao, Xiaohui; Li, Junhao; Xu, Guanghui; Sheng, Meiping. 2020. "Ship Type Classification by Convolutional Neural Networks with Auditory-Like Mechanisms" Sensors 20, no. 1: 253.

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