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Cited by 55Underwater Acoustics ResearchMusic and Audio ProcessingMarine animal studies overview

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

Open access
Shen, Sheng, Yang, Honghui, Li, Junhao, Xu, Guanghui, Sheng, Meiping·2018·Entropy
DOI 10.3390/e20120990PMC PMC7512589
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Abstract

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 signal. The proposed model includes three parts. The first part is performed by a multi-scale 1D time convolutional layer initialized by auditory filter banks. Signals are decomposed into frequency components by convolution operation. In the second part, the decomposed signals are converted into frequency domain by permute layer and energy pooling layer to form frequency distribution in auditory cortex. Then, 2D frequency convolutional layers are applied to discover spectro-temporal patterns, as well as preserve locality and reduce spectral variations in ship noise. In the third part, the whole model is optimized with an objective function of classification to obtain appropriate auditory filters and feature representations that are correlative with ship categories. The optimization reflects the plasticity of auditory system. Experiments on five ship types and background noise show that the proposed approach achieved an overall classification accuracy of 79.2%, which improved by 6% compared to conventional approaches. Auditory filter banks were adaptive in shape to improve accuracy of classification.

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  • The auditory-inspired CNN had a high accuracy (87.2%), outperforming a standard CNN (83.2%), a stack of autoencoders (81.4%), wavelet transform with PCA (74.6%), and a support-vector machine (68.2%).

    “The results showed that the proposed auditory-inspired CNN had a high accuracy (87.2%), outperforming a standard CNN (83.2%), a stack of autoencoders [113] (81.4%) and various traditional object recognition algorithms, such as a combination of wavelet transform with PCA [114] (74.6%) and a support-vector machine [115] (68.2%).”

    From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [103]

    Section 5.2 (page unavailable) reports 79.2% accuracy for the auditory-inspired CNN and 73.2% for the spectral-feature CNN, contrary to the claimed 87.2% and 83.2%. The source supports the general finding that the proposed model outperformed its CNN baseline, but the other named methods and their claimed accuracies are not reported in the cited passage. (LLM, conf=0.90)

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References 33 · Cited by 55

Research works printed but not linked: 28

  1. [1]Meng Q. Yang S. Piao S. The classification of underwater acoustic target signals based on wave structure and support vector machine J. Acoust. Soc. Am. 2014 136 2265 10.1121/1.4900181 · DOI
  2. [2]Meng Q. Yang S. A wave structure based method for recognition of marine acoustic target signals J. Acoust. Soc. Am. 2015 137 2242 10.1121/1.4920186 · DOI
  3. [4]Siddagangaiah S. Li Y. Guo X. Chen X. Zhang Q. Yang K. Yang Y. A complexity-based approach for the detection of weak signals in ocean ambient noise Entropy 2016 18 101 10.3390/e18030101 · DOI
  4. [5]Das A. Kumar A. Bahl R. Marine vessel classification based on passive sonar data: The cepstrum-based approach Iet Radar Sonar Navig. 2013 7 87 93 10.1049/iet-rsn.2011.0142 · DOI
  5. [6]Santos-Domínguez D. Torres-Guijarro S. Cardenal-López A. Pena-Gimenez A. ShipsEar: An underwater vessel noise database Appl. Acoust. 2016 113 64 69 10.1016/j.apacoust.2016.06.008 · DOI
  6. [8]Zhang L. Wu D. Han X. Zhu Z. Feature extraction of underwater target signal using Mel frequency cepstrum coefficients based on acoustic vector sensor J. Sens. 2016 2016 7864213 10.1155/2016/7864213 · DOI
  7. [9]Smith E.C. Lewicki M.S. Efficient auditory coding Nature 2006 439 978 982 10.1038/nature04485 16495999 · DOI
  8. [10]Yang H. Gan A. Chen H. Pan Y. Underwater acoustic target recognition using SVM ensemble via weighted sample and feature selection Proceedings of the International Bhurban Conference on Applied Sciences and Technology Islamabad, Pakistan 12–16 January 2016 IEEE Piscataway, NJ, USA 2016 522 527
  9. [11]Filho W.S. Seixas J.M.D. Moura N.N.D. Preprocessing passive sonar signals for neural classification IET Radar Sonar Navig. 2011 5 605 612 10.1049/iet-rsn.2010.0157 · DOI
  10. [13]Damianos K. Jan S. Richard S. William H. John M. Individual Ship Detection Using Underwater Acoustics Proceedings of the International Conference on Acoustics, Speech, and Signal Processing Calgary, AB, Canada 15–20 April 2018 IEEE Piscataway, NJ, USA 2018 2121 2125
  11. [14]Damianos K. William H. Richard S. John M. Stavros T. Edin I. George S. Applying speech technology to the ship-type classification problem Proceedings of the OCEANS 2017 Anchorage, AK, USA 18–21 September 2017 IEEE Piscataway, NJ, USA 2018 1 8
  12. [15]Filho J.B.O.S. Seixas J.M.D. Class-modular multi-layer perceptron networks for supporting passive sonar signal classification IET Radar Sonar Navig. 2016 10 311 317 10.1049/iet-rsn.2015.0179 · DOI
  13. [16]Sainath T.N. Kingsbury B. Mohamed A.R. Ramabhadran B. Learning filter banks within a deep neural network framework Proceedings of the 2013 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) Olomouc, Czech Republic 8–12 December 2013 IEEE Piscataway, NJ, USA 2014 297 302
  14. [17]Kamal S. Mohammed S.K. Pillai P.R.S. Supriya M.H. Deep learning architectures for underwater target recognition Proceedings of the 2013 International Symposium on Ocean Electronics Kochi, India 23–25 October 2013 IEEE Piscataway, NJ, USA 2014 48 54
  15. [19]Yang H. Shen S. Yao X. Sheng M. Wang C. Competitive deep-belief networks for underwater acoustic target recognition Sensors 2018 18 952 10.3390/s18040952 PMC5948803 29570642 · DOI
  16. [20]Shen S. Yang H. Sheng M. Compression of a deep competitive network based on mutual information for underwater acoustic targets recognition Entropy 2018 20 243 10.3390/e20040243 PMC7512758 33265334 · DOI
  17. [21]Mu L. Peng Y. Qiu M. Yang X. Hu C. Zhang F. Study on modulation spectrum feature extraction of ship radiated noise based on auditory model Proceedings of the 2016 IEEE/OES China Ocean Acoustics Harbin, China 9–11 January 2016 IEEE Piscataway, NJ, USA 2016 1 5
  18. [22]Ghosh J. Deuser L. Beck S.D. A neural network based hybrid system for detection, characterization, and classification of short-duration oceanic signals IEEE J. Ocean. Eng. 2002 17 351 363 10.1109/48.180304 · DOI
  19. [24]Moore B.C.J. Cochlear Hearing Loss: Physiological, Psychological and Technical Issues 2nd ed. John Wiley and Sons Hoboken, NJ, USA 2008
  20. [25]Gelf S.A. Hearing: An Introduction to Psychological and Physiological Acoustics CRC Press Boca Raton, FL, USA 2009 320
  21. [27]Glasberg B.R. Moore B.C. Derivation of auditory filter shapes from notched-noise data Hear. Res. 1990 47 103 138 10.1016/0378-5955(90)90170-T 2228789 · DOI
  22. [28]Arora S. Bhaskara A. Ge R. Ma T. Provable bounds for learning some deep representations Proceedings of the International Conference on Machine Learning Beijing, China 21–26 June 2014 JMLR, Inc. Cambridge, MA, USA 584 592
  23. [29]Smith E.C. Lewicki M.S. Efficient coding of time-relative structure using spikes Neural Comput. 2005 17 19 45 10.1162/0899766052530839 15563746 · DOI
  24. [30]Chechik G. Nelken I. Auditory abstraction from spectro-temporal features to coding auditory entities Proc. Natl. Acad. Sci. USA 2012 109 18968 18973 10.1073/pnas.1111242109 23112145 PMC3503225 · DOI
  25. [31]Decharms R.C. Blake D.T. Merzenich M.M. Optimizing sound features for cortical neurons Science 1998 280 1439 1443 10.1126/science.280.5368.1439 9603734 · DOI
  26. [32]Baqar M. Zaidi S.S.H. Performance evaluation of linear and multi-linear subspace learning techniques for object classification based on underwater acoustics Proceedings of the International Bhurban Conference on Applied Sciences and Technology Islamabad, Pakistan 10–14 January 2017 IEEE Piscataway, NJ, USA 675 683
  27. [33]Meddis R. Lopez-Poveda E.A. Auditory Periphery: From Pinna to Auditory Nerve Springer Boston, MA, USA 2010 7 38
  28. [34]Mckenna M.F. Ross D. Wiggins S.M. Hildebrand J.A. Underwater radiated noise from modern commercial ships J. Acoust. Soc. Am. 2012 131 92 103 10.1121/1.3664100 22280574 · DOI

Authors
Shen, S., Yang, H., Li, J., Xu, G., & Sheng, M.
Affiliations
Northwestern Polytechnical University (CN)
Corresponding author
Yang, Honghui
ORCID
Venue
Entropy
Publisher
MDPI AG
Published
2018-12-19
Volume
20
Issue
12
Pages
990
ISSN
1099-4300 (ISSN-L)
DOI
10.3390/e20120990
PMC
PMC7512589
OpenAlex
W2905385593
Cited by
55
References
33
Citations by year
2026: 6 · 2025: 3 · 2024: 14 · 2023: 8 · 2022: 8 · 2021: 10 · 2020: 1 · 2019: 5
FWCI
2.57
Type
journal article
Language
en
Open access
Licence
Locations
  • Entropy (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 (51179157)
Topics
  • Physical Sciences › Earth and Planetary Sciences › Oceanography › Underwater Acoustics Research
  • Physical Sciences › Computer Science › Signal Processing › Music and Audio Processing
  • Physical Sciences › Environmental Science › Ecology › Marine animal studies overview
Keywords
ship classification, underwater acoustic signals, hydrophone data, auditory filterbank, ship radiated noise, ship detection

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