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Cited by 71Underwater Acoustics ResearchUnderwater Vehicles and Communication SystemsMusic and Audio Processing

Convolutional neural networks for passive monitoring of a shallow water environment using a single sensor

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Ferguson, Eric L., Ramakrishnan, Rishi, Williams, Stefan B., Jin, Craig T.·2017·2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
DOI 10.1109/icassp.2017.7952638
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Abstract

A cost effective approach to remote monitoring of protected areas such as marine reserves and restricted naval waters is to use passive sonar to detect, classify, localize, and track marine vessel activity (including small boats and autonomous underwater vehicles). Cepstral analysis of underwater acoustic data enables the time delay between the direct path arrival and the first multipath arrival to be measured, which in turn enables estimation of the instantaneous range of the source (a small boat). However, this conventional method is limited to ranges where the Lloyd's mirror effect (interference pattern formed between the direct and first multipath arrivals) is discernible. This paper proposes the use of convolutional neural networks (CNNs) for the joint detection and ranging of broadband acoustic noise sources such as marine vessels in conjunction with a data augmentation approach for improving network performance in varied signal-to-noise ratio (SNR) situations. Performance is compared with a conventional passive sonar ranging method for monitoring marine vessel activity using real data from a single hydrophone mounted above the sea floor. It is shown that CNNs operating on cepstrum data are able to detect the presence and estimate the range of transiting vessels at greater distances than the conventional method.

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  • Interference of the direct path and the surface reflected path creates the Lloyd mirror pattern, which makes object detection and classification in shallow waters challenging.

    “The interference of these two paths creates the so-called Lloyd mirror pattern [24], which makes object detection and classification in shallow waters (such as in a harbour or port) a challenging task [25]”

    From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [25]

    The Introduction (p. 1) describes clutter, noise, and multipath reflection in shallow waters such as harbours and ports, and says conventional time-delay estimation is limited when the Lloyd’s mirror effect is not discernible. This supports the general difficulty of acoustic monitoring there, but the paper links the effect specifically to limitations in ranging, not to difficulty in both object detection and classification. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • The combination of cepstral filtering and CNNs greatly extends detection ranges and robustness to noise compared to an algorithmic method, but has slightly reduced accuracy at short range due to expressive bias.

    “The combination of cepstral filtering and CNNs greatly extends detection ranges and robustness to noise compared to an algorithmic method, but has slightly reduced accuracy at short range due to expressive bias [25].”

    From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [25]

    The Comparison of Range and Detection Methods (p. 4) reports that CNNs estimate vessel range beyond 180 m, where the algorithmic method fails, but have greater error and significant bias below 180 m. The Conclusions (p. 4) describe robustness to changes in SNR. Neither passage attributes the short-range bias to an expressive limitation, and the paper calls the bias significant rather than slight. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • Classical signal processing methods such as cepstral analysis can attenuate some effects of reflection interference and scattering losses, but only at short ranges with a high signal-to-noise ratio.

    “Classical signal processing methods, such as cepstral analysis [13], are able to attenuate some of the effects of reflection interference and scattering losses, but only at short ranges with a high signal-to-noise ratio [25]”

    From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [25]

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Research works printed but not linked: 11

  1. [2][2] G.C. Carter, Ed., Coherence and Time Delay Estimation , IEEE Press, New York, 1993. -
  2. [3][3] Y.T. Chan and K.C. Ho, 'A simple and efficient estimator for hyperbolic location,' IEEE Trans. Signal Proc. , vol. 42, pp. 1905-1915, 1994. -
  3. [4][4] J.Benesty, J.Chen, and Y.Huang, 'Time-delay estimation via linear interpolation and cross correlation,' IEEE Transactions on Speech and Audio Processing , vol. 12, pp. 509-519, September 2004. -
  4. [5][5] M. Hamilton and P.M. Schultheiss, 'Passive ranging in multipath dominant environments, part 1: Known multipath parameters,' IEEE Transactions on Signal Processing , vol. 40, no. 1, pp. 1-12, 1992. -
  5. [6][6] B.G. Ferguson, K.W. Lo, and R.A. Thuraisingham, 'Sensor position estimation and source ranging in a shallow water environment,' IEEE Journal of Oceanic Engineering , 2005. -
  6. [9][9] Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik, 'Rich feature hierarchies for accurate object detection and semantic segmentation,' in Proceedings of the IEEE conference on computer vision and pattern recognition , 2014, pp. 580-587. -
  7. [10][10] A. Valada, L. Spinello, and W. Burgard, 'Deep feature learning for acoustics-based terrain classification,' in Proceedings of the International Symposium on Robotics Research , Genova, Italy, 2015. -
  8. [11][11] K.W. Lo, B.G. Ferguson, Y. Gao, and A. Maguer, 'Aircraft flight parameter estimation using acoustic multipath delays,' IEEE Trans. on Aero. and Elect. Systems , vol. 39, pp. 259-268, 2003. -
  9. [12][12] B.P. Bogert, M.J.R. Healy, and J.W. Tukey, 'The quefrency analysis of time series for echoes: Cepstrum, pseudoautocovariance, cross-cepstrum, and saphe cracking,' in Proceedings of the symposium on time series analysis , New York, N.Y, 1963, vol. 15. -
  10. [13][13] A.V. OppenHeim and R.W. Schafer, 'From freqency to quefrency: a history of the cepstrum,' IEEE Signal Processing Magazine , vol. 21, pp. 95-106, 2004. -
  11. [15][15] Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov, 'Dropout: A simple way to prevent neural networks from overfitting,' J. Mach. Learn. Res. , vol. 15, no. 1, pp. 1929-1958, jan 2014.

Cited by

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Authors
Ferguson, E. L., Ramakrishnan, R., Williams, S. B., & Jin, C. T.
Affiliations
The University of Sydney (AU)
ORCID
Venue
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Publisher
IEEE
Published
2017-03
Pages
2657-2661
ISSN
1520-6149 (ISSN-L)
DOI
10.1109/icassp.2017.7952638
OpenAlex
W2561682006
Cited by
71
References
2
Citations by year
2026: 1 · 2025: 8 · 2024: 9 · 2023: 6 · 2022: 12 · 2021: 10 · 2020: 9 · 2019: 9 · 2018: 6 · 2017: 1
FWCI
48.16
Type
conference paper
Language
en
Open access
Locations
  • IEEE International Conference on Acoustics Speech and Signal Processing (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
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
passive sonar, convolutional neural networks, cepstral analysis, underwater acoustic ranging, multipath delay estimation, single hydrophone, autonomous underwater vehicles, data augmentation, signal-to-noise ratio

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