Auditory Inspired Convolutional Neural Networks for Ship Type Classification with Raw Hydrophone Data

Auditory Inspired Convolutional Neural Networks for Ship Type Classification with Raw Hydrophone Data

Detecting and classifying ships based on radiated noise provide practical guidelines for the reduction of underwater noise footprint of shipping. In this paper, the detection and classification are implemented by auditory inspired convolutional neural networks trained from raw underwater acoustic si...

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Bibliographic Details
Main Authors: Sheng Shen, Honghui Yang, Junhao Li, Guanghui Xu, Meiping Sheng
Format: Article
Language:English
Published: MDPI AG 2018-12-01
Series:Entropy
Subjects:
convolutional neural network
deep learning
auditory
ship radiated noise
hydrophone
Online Access:https://www.mdpi.com/1099-4300/20/12/990

Internet

https://www.mdpi.com/1099-4300/20/12/990

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