Improving the performance of underwater acoustic signal recognition using modified residual convolutional neural network



  • Doan Van Sang Vietnam Naval Academy
  • Vi Cong Doan Vietnam Naval Academy
  • Tran Phu Ninh Vietnam Naval Academy
  • Nguyen Van Tien Institute of System Integration, Military Technical Academy
  • Tran Cong Trang (Corresponding Author) Vietnam Naval Academy



Artificial neural network; ResNet model; Underwater acoustic signal classification; Passive sonar.


This paper presents the research results of an underwater acoustic signal recognition model using a convolutional neural network based on the residual structure, which is modified from the ResNet model to increase the performance in terms of processing speed while ensuring high recognition accuracy. Compared with the original ResNet model and some other existing models, the modified ResNet model provided a good recognition performance in terms of correct signal source recognition rate and increased prediction speed.


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How to Cite

Đoàn Văn Sáng, Vi Công Đoàn, Trần Phú Ninh, Nguyễn Văn Tiến, and Trần Công Tráng. “Improving the Performance of Underwater Acoustic Signal Recognition Using Modified Residual Convolutional Neural Network”. Journal of Military Science and Technology, no. 81, Aug. 2022, pp. 53-59, doi:10.54939/1859-1043.j.mst.81.2022.53-59.



Research Articles