International Journal Of Coastal, Offshore And Environmental Engineering(ijcoe)

International Journal Of Coastal, Offshore And Environmental Engineering(ijcoe)

Underwater Ship-radiated Acoustic Noise Recognition Based on Mel-Spectrogram and Convolutional Neural Network

Document Type : Original Research Article

Author
Mohammad Reza Khalilabadi
Faculty of Naval Aviation, Malek Ashtar University of Technology
10.22034/ijcoe.2023.166732
Abstract
The abstract should include the One of the most exciting topics for researchers over the past few years is detecting underwater acoustic noises. Meanwhile, the complicated nature of the ocean makes this task very challenging. Also, making signals formatted data compatible with machine learning approaches needs much knowledge in signal processing for feature detection. This paper proposed a method to overcome these challenges, which extracts features with Convolutional Neural Network (CNN) and Mel-spectrogram (converting signal data to images). This method needless knowledge in signal processing and more knowledge in machine learning; because using CNNs find the hidden pattern and knowledge of the data automatically. The proposed approach detected the presence of the ships and categorized them into different kinds of them with 99% accuracy that is a noticeable improvement considering state of the art. The performed CNN models consist of 2 CNN layers for feature extraction and a Dense layer for classification the underwater ship noises.
Keywords
Underwater Acoustic
Deep Learning
Recognition
Noise
CNN

This is an open access article under the CC BY license

  1. M. R. Khalilabadi, “Underwater Terrain and Gravity aided inertial navigation based on Kalman filter,” Int. J. Coast. Offshore Eng., vol. 5, no. 3, pp. 15–21, 2022.
  2. M. R. Khalilabadi, “The effect of meteorological events on sea surface height variations along the northwestern Persian Gulf.,” Curr. Sci. 00113891, vol. 110, no. 11, 2016.
  3. M. R. Khalilabadi, “‎ 3D modeling of Circulation in the Oman Sea Using the MITgcm Model,” Hydrophysics, vol. 2, no. 1, pp. 61–68, 2016.
  4. M. R. Khalilabadi, “Tide–surge interaction in the Persian Gulf, Strait of Hormuz and the Gulf of Oman,” Weather, vol. 71, no. 10, pp. 256–261, 2016.
  5. M. R. Khalilabadi and M. Akbari Nasab, “Study of static stability and double diffusion in the Oman Sea,” Iran. J. Mar. Sci. Technol., vol. 18, no. 71, pp. 11–19, 2014.
  6. M. R. Khalilabadi and B. Behrooz, “Simulation of volumetric flow at the Arvandrood estuary using the MIKE21 model,” Hydrophysics, vol. 5, no. 2, pp. 1–10, 2020.
  7. M. R. Khalilabadi and B. S. H. HASSANTABAR, “Investigation of magnetic field fluctuations due to sea waves in the Strait of Hormuz,” 2016.
  8. M. R. Khalilabadi and D. Mansouri, “Effect of super cyclone ‘GONU’ on sea level variation along Iranian coastlines,” 2013.
  9. M. R. Khalilabadi and H. Shahmirzaee, “Marine Magnetic Data Processing and Extracting Magnetic Anomaly,” Hydrophysics, vol. 3, no. 1, pp. 1–10, 2017.
  10. M. R. Khalilabadi, M. Sadrinassab, V. Chegini, and M. Akbarinassab, “Internal Wave Generation in the Gulf of Oman (Outflow of Persian Gulf),” 2015.
  11. M. Akbarinasab, M. R. Khalilabadi, and M. M. Moghadam, “Impact of relative vorticity on sea level fluctuations on the South coast of Caspian Sea.,” Casp. J. Appl. Sci. Res., vol. 5, no. 4, 2016.
  12. A. Ghorbani and M. R. Khalilabadi, “Positioning Using Classification and Regression: Case study of Oman Sea,” Int. J. Coast. Offshore Eng., vol. 4, no. 3, pp. 35–41, 2020.
  13. S. H. Hosseini, M. Akbarinasab, and M. R. Khalilabadi, “Numerical simulation of the effect internal tide on the propagation sound in the Oman Sea,” 2018.
  14. M. R. Khalilabadi, “2D Modeling of Wave Propagation in Shallow Water by the Method of Characteristics,” Arch. Acoust., vol. 47, no. 3, pp. 407–412, 2022.
  15. M. R. Khalilabadi, “An autonomous location prediction model for maritime transport applications: a case study of Persian Gulf,” Ships Offshore Struct., pp. 1–8, 2022.
  16. A. Das, A. Kumar, and R. Bahl, “Marine vessel classification based on passive sonar data: the cepstrum‐based approach,” IET Radar Sonar Navig., vol. 7, no. 1, pp. 87–93, 2013.
  17. D. Santos-Domínguez, S. Torres-Guijarro, A. Cardenal-López, and A. Pena-Gimenez, “ShipsEar: An underwater vessel noise database,” Appl. Acoust., vol. 113, pp. 64–69, 2016.
  18. X. Wei, L. I. Gang-Hu, and Z. Q. Wang, “Underwater target recognition based on wavelet packet and principal component analysis,” Comput Simul, vol. 28, pp. 8–290, 2011.
  19. H. Yang, S. Shen, X. Yao, M. Sheng, and C. Wang, “Competitive deep-belief networks for underwater acoustic target recognition,” Sensors, vol. 18, no. 4, p. 952, 2018.
  20. L. Zhang, D. Wu, X. Han, and Z. Zhu, “Feature extraction of underwater target signal using mel frequency cepstrum coefficients based on acoustic vector sensor,” J. Sens., vol. 2016, 2016.
  21. S. Shen, H. Yang, and J. Li, “Improved auditory inspired convolutional neural networks for ship type classification,” in OCEANS 2019-Marseille, 2019, pp. 1–4.
  22.  X. Cao, X. Zhang, Y. Yu, and L. Niu, “Deep learning-based recognition of underwater target,” in 2016 IEEE International Conference on Digital Signal Processing (DSP), 2016, pp. 89–93.
  23. F. Yuan, X. Ke, and E. Cheng, “Joint representation and recognition for ship-radiated noise based on multimodal deep learning,” J. Mar. Sci. Eng., vol. 7, no. 11, p. 380, 2019.
  24. X. Ke, F. Yuan, and E. Cheng, “Integrated optimization of underwater acoustic ship-radiated noise recognition based on two-dimensional feature fusion,” Appl. Acoust., vol. 159, p. 107057, 2020.
  25. K. Lavanya, C. L. Devi, M. D. Sree, and P. N. Shareef, “Predicting the Emotions Based on Emoji’s and Speech Using Machine Learning Techniques,” 2021.
  26. M. V. Valueva, N. N. Nagornov, P. A. Lyakhov, G. V. Valuev, and N. I. Chervyakov, “Application of the residue number system to reduce hardware costs of the convolutional neural network implementation,” Math. Comput. Simul. vol. 177, pp. 232–243, 2020.
  27. I. M. Dheir, A. S. A. Mettleq, A. A. Elsharif, and S. S. Abu-Naser, “Classifying nuts types using convolutional neural network,” Int. J. Acad. Inf. Syst. Res. IJAISR, vol. 3, no. 12, 2020.
  28. H. H. Aghdam and E. J. Heravi, “Guide to convolutional neural networks,” N. Y. NY Springer, vol. 10, no. 978–973, p. 51, 2017.
  29. D. M. Powers, “Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation,” ArXiv Prepr. ArXiv201016061, 2020.