A Deep Convolutional Neural Network Inspired by Auditory Perception for Underwater Acoustic Target Recognition
Underwater acoustic target recognition (UATR) using ship-radiated noise faces big challenges due to the complex marine environment. In this paper, inspired by neural mechanisms of auditory perception, a new end-to-end deep neural network named auditory perception inspired Deep Convolutional Neural N...
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doaj-2fd3ebf3652c468797cf968757212fea2020-11-24T23:32:08ZengMDPI AGSensors1424-82202019-03-01195110410.3390/s19051104s19051104A Deep Convolutional Neural Network Inspired by Auditory Perception for Underwater Acoustic Target RecognitionHonghui Yang0Junhao Li1Sheng Shen2Guanghui Xu3School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, ChinaUnderwater acoustic target recognition (UATR) using ship-radiated noise faces big challenges due to the complex marine environment. In this paper, inspired by neural mechanisms of auditory perception, a new end-to-end deep neural network named auditory perception inspired Deep Convolutional Neural Network (ADCNN) is proposed for UATR. In the ADCNN model, inspired by the frequency component perception neural mechanism, a bank of multi-scale deep convolution filters are designed to decompose raw time domain signal into signals with different frequency components. Inspired by the plasticity neural mechanism, the parameters of the deep convolution filters are initialized randomly, and the is n learned and optimized for UATR. The n, max-pooling layers and fully connected layers extract features from each decomposed signal. Finally, in fusion layers, features from each decomposed signal are merged and deep feature representations are extracted to classify underwater acoustic targets. The ADCNN model simulates the deep acoustic information processing structure of the auditory system. Experimental results show that the proposed model can decompose, model and classify ship-radiated noise signals efficiently. It achieves a classification accuracy of 81.96%, which is the highest in the contrast experiments. The experimental results show that auditory perception inspired deep learning method has encouraging potential to improve the classification performance of UATR.http://www.mdpi.com/1424-8220/19/5/1104underwater acoustic target recognitionship-radiated noisedeep learningbrain-inspiredauditory perception inspiredfilter learning |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Honghui Yang Junhao Li Sheng Shen Guanghui Xu |
spellingShingle |
Honghui Yang Junhao Li Sheng Shen Guanghui Xu A Deep Convolutional Neural Network Inspired by Auditory Perception for Underwater Acoustic Target Recognition Sensors underwater acoustic target recognition ship-radiated noise deep learning brain-inspired auditory perception inspired filter learning |
author_facet |
Honghui Yang Junhao Li Sheng Shen Guanghui Xu |
author_sort |
Honghui Yang |
title |
A Deep Convolutional Neural Network Inspired by Auditory Perception for Underwater Acoustic Target Recognition |
title_short |
A Deep Convolutional Neural Network Inspired by Auditory Perception for Underwater Acoustic Target Recognition |
title_full |
A Deep Convolutional Neural Network Inspired by Auditory Perception for Underwater Acoustic Target Recognition |
title_fullStr |
A Deep Convolutional Neural Network Inspired by Auditory Perception for Underwater Acoustic Target Recognition |
title_full_unstemmed |
A Deep Convolutional Neural Network Inspired by Auditory Perception for Underwater Acoustic Target Recognition |
title_sort |
deep convolutional neural network inspired by auditory perception for underwater acoustic target recognition |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2019-03-01 |
description |
Underwater acoustic target recognition (UATR) using ship-radiated noise faces big challenges due to the complex marine environment. In this paper, inspired by neural mechanisms of auditory perception, a new end-to-end deep neural network named auditory perception inspired Deep Convolutional Neural Network (ADCNN) is proposed for UATR. In the ADCNN model, inspired by the frequency component perception neural mechanism, a bank of multi-scale deep convolution filters are designed to decompose raw time domain signal into signals with different frequency components. Inspired by the plasticity neural mechanism, the parameters of the deep convolution filters are initialized randomly, and the is n learned and optimized for UATR. The n, max-pooling layers and fully connected layers extract features from each decomposed signal. Finally, in fusion layers, features from each decomposed signal are merged and deep feature representations are extracted to classify underwater acoustic targets. The ADCNN model simulates the deep acoustic information processing structure of the auditory system. Experimental results show that the proposed model can decompose, model and classify ship-radiated noise signals efficiently. It achieves a classification accuracy of 81.96%, which is the highest in the contrast experiments. The experimental results show that auditory perception inspired deep learning method has encouraging potential to improve the classification performance of UATR. |
topic |
underwater acoustic target recognition ship-radiated noise deep learning brain-inspired auditory perception inspired filter learning |
url |
http://www.mdpi.com/1424-8220/19/5/1104 |
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