Multi-Features Fusion for Underwater Acoustic Target Recognition based on Convolution Recurrent Neural Networks-Reference-Cited by-同舟云学术

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Multi-Features Fusion for Underwater Acoustic Target Recognition based on Convolution Recurrent Neural Networks

Published:2022-08-24 Issue: Volume: Page:
ISSN:
Container-title:2022 8th International Conference on Big Data and Information Analytics (BigDIA)
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Author:

Zhang Wen¹,Lin Bin¹,Yan Yulin¹,Zhou Aolong²,Ye Yanqing¹,Zhu Xiaomin³

Affiliation:

1. Strategic Evaluation and Consultation Center, Academy of Military Sciences,Beijing,China

2. College of Computer Science and Technology, National University of Defense Technology,Changsha,China

3. College of System Engineering, National University of Defense Technology,Changsha,China

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9873850/9873995/09874151.pdf?arnumber=9874151

Reference10 articles.

1. A denoising representation framework for underwater acoustic signal recognition

2. Underwater Target Recognition Based on Multi-Decision LOFAR Spectrum Enhancement: A Deep-Learning Approach

3. ShipsEar: An underwater vessel noise database

4. DeepShip: An underwater acoustic benchmark dataset and a separable convolution based autoencoder for classification

5. Bark-wavelet Analysis and Hilbert–Huang Transform for Underwater Target Recognition

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1. A Novel Multi-Feature Fusion Model Based on Pre-Trained Wav2vec 2.0 for Underwater Acoustic Target Recognition;Remote Sensing;2024-07-03

2. Feature Extraction of Underwater Acoustic Signal Target Using Machine Learning Technique;Traitement du Signal;2024-06-26

3. A survey on machine learning in ship radiated noise;Ocean Engineering;2024-04

4. Underwater acoustic target recognition method based on WA-DS decision fusion;Applied Acoustics;2024-02

5. A ship-radiated noise classification method based on domain knowledge embedding and attention mechanism;Engineering Applications of Artificial Intelligence;2024-01

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