Deep convolution stack for waveform in underwater acoustic target recognition

Tian, Shengzhao; Chen, Duanbing; Wang, Hang; Liu, Jingfa

doi:10.1038/s41598-021-88799-z

Cited by 86Underwater Acoustics ResearchUnderwater Vehicles and Communication SystemsMusic and Audio Processing

Deep convolution stack for waveform in underwater acoustic target recognition

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Tian, Shengzhao, Chen, Duanbing, Wang, Hang, Liu, Jingfa·2021·Scientific Reports

DOI 10.1038/s41598-021-88799-zPMC PMC8099869

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Abstract

In underwater acoustic target recognition, deep learning methods have been proved to be effective on recognizing original signal waveform. Previous methods often utilize large convolutional kernels to extract features at the beginning of neural networks. It leads to a lack of depth and structural imbalance of networks. The power of nonlinear transformation brought by deep network has not been fully utilized. Deep convolution stack is a kind of network frame with flexible and balanced structure and it has not been explored well in underwater acoustic target recognition, even though such frame has been proven to be effective in other deep learning fields. In this paper, a multiscale residual unit (MSRU) is proposed to construct deep convolution stack network. Based on MSRU, a multiscale residual deep neural network (MSRDN) is presented to classify underwater acoustic target. Dataset acquired in a real-world scenario is used to verify the proposed unit and model. By adding MSRU into Generative Adversarial Networks, the validity of MSRU is proved. Finally, MSRDN achieves the best recognition accuracy of 83.15%, improved by 6.99% from the structure related networks which take the original signal waveform as input and 4.48% from the networks which take the time-frequency representation as input.

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MSRDN was developed to classify passive underwater acoustic signals by modelling the original signal waveform directly.
“A multiscale residual deep neural network (MSRDN) (inspired by [94]) was developed in [93] to classify passive underwater acoustic signals by modelling the original signal waveform directly.”
From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [93]
Tests using the ONC public dataset showed that MSRDN outperformed other DL methods whose inputs were based on time-frequency representations.
“Tests using Ocean Networks Canada (https://data.oceannetworks.ca (accessed on 20 December 2021)) (ONC) public dataset have shown that MSRDN outperformed other DL methods whose inputs were based on time–frequency representations.”
From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [93]

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Self-citation

Authors: Tian, S., Chen, D., Wang, H., & Liu, J.
Affiliations: University of Electronic Science and Technology of China (CN)
Guangdong University of Foreign Studies (CN)
Corresponding author: Liu, Jingfa
ORCID: Tian, Shengzhao
Chen, Duanbing
Wang, Hang
Liu, Jingfa
Venue: Scientific Reports
Publisher: Springer Science and Business Media LLC
Published: 2021-05-05
Volume: 11
Issue: 1
Pages: 9614-9614
ISSN: 2045-2322 (ISSN-L)
DOI: 10.1038/s41598-021-88799-z
PMC: PMC8099869
OpenAlex: W3157467987
Cited by: 86
References: 63
Citations by year: 2026: 17 · 2025: 21 · 2024: 17 · 2023: 15 · 2022: 14 · 2021: 2
FWCI: 5.77
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor)
Locations: Scientific Reports (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: National Natural Science Foundation of China (Y03111023901014006, 61673085)
University of Electronic Science and Technology of China (Y03111023901014006)
Topics: Physical Sciences › Earth and Planetary Sciences › Oceanography › Underwater Acoustics Research
Physical Sciences › Engineering › Ocean Engineering › Underwater Vehicles and Communication Systems
Physical Sciences › Computer Science › Signal Processing › Music and Audio Processing
Keywords: underwater acoustic target recognition, ResNet, generative adversarial networks

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Deep convolution stack for waveform in underwater acoustic target recognition

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