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Cited by 98Underwater Acoustics ResearchUnderwater Vehicles and Communication SystemsMusic and Audio Processing

Underwater Acoustic Target Recognition: A Combination of Multi-Dimensional Fusion Features and Modified Deep Neural Network

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Wang, Xingmei, Liu, Anhua, Zhang, Yu, Xue, Fuzhao·2019·Remote Sensing
DOI 10.3390/rs11161888
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Abstract

A method with a combination of multi-dimensional fusion features and a modified deep neural network (MFF-MDNN) is proposed to recognize underwater acoustic targets in this paper. Specifically, due to the complex and changeable underwater environment, it is difficult to describe underwater acoustic signals with a single feature. The Gammatone frequency cepstral coefficient (GFCC) and modified empirical mode decomposition (MEMD) are developed to extract multi-dimensional features in this paper. Moreover, to ensure the same time dimension, a dimension reduction method is proposed to obtain multi-dimensional fusion features in the original underwater acoustic signals. Then, to reduce redundant features and further improve recognition accuracy, the Gaussian mixture model (GMM) is used to modify the structure of a deep neural network (DNN). Finally, the proposed underwater acoustic target recognition method can obtain an accuracy of 94.3% under a maximum of 800 iterations when the dataset has underwater background noise with weak targets. Compared with other methods, the recognition results demonstrate that the proposed method has higher accuracy and strong adaptability.

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  • Results obtained with the method in [106] suggest that not using a CNN to learn the features saves computing time, as no redundant features are generated.

    “Results obtained with this method suggest that by not using a CNN to learn the features saves computing time, as no redundant features are generated.”

    From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [106]

    The Introduction says that directly using a DNN can generate redundant features and waste computing resources. Section 3 says the paper uses a GMM to reduce redundant features and the size of its modified DNN. This supports the resource-saving rationale, but the source describes a DNN rather than a CNN and does not report a measured computing-time saving. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • Comparisons in [106] were only executed against variations of the authors' own algorithm, and not with respect to state-of-the-art CNNs.

    “However, comparisons were only executed against variations of the authors’ own algorithm, and not with respect to state-of-the-art CNNs.”

    From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [106]

    Table 2 (experimental comparisons; page not specified in our copy) lists no CNN baseline, supporting that part of the claim. But it also compares the proposed method with MFCC-GMM and GFCC-GMM, which are not merely variations of the authors’ modified neural network; the word “only” overstates the comparison’s scope. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 36

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Cited by

Sorted by citations

Authors
Wang, X., Liu, A., Zhang, Y., & Xue, F.
Affiliations
  • Harbin Engineering University (CN)
  • Harbin Institute of Technology (CN)
Corresponding author
Wang, Xingmei
ORCID
Venue
Remote Sensing
Publisher
MDPI AG
Published
2019-08-13
Volume
11
Issue
16
Pages
1888
ISSN
2072-4292 (ISSN-L)
DOI
10.3390/rs11161888
OpenAlex
W2967719571
Cited by
98
References
0
Citations by year
2026: 11 · 2025: 13 · 2024: 22 · 2023: 19 · 2022: 20 · 2021: 5 · 2020: 8
FWCI
4.56
Type
journal article
Language
en
Open access
Licence
Locations
  • Remote Sensing (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Natural Science Foundation of China (41876110)
  • Fundamental Research Funds for the Central Universities (3072019CFT0602)
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, multi-dimensional feature fusion, Gaussian mixture model, deep learning architectures, dimensionality reduction, underwater acoustic signals

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