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An efficient deep learning approach with frequency and channel optimization for underwater acoustic target
Di Zeng1,2, Shefeng Yan1,2, Jirui Yang1,2
1University of Chinese Academy of Sciences, Beijing, 101408, China.
Scientific Reports
|July 27, 2025
Summary
This study introduces FCResNet5, a new deep learning model for ship radiated noise recognition. It offers a computationally efficient and accurate solution for underwater acoustic signal classification.
Area of Science:
- Acoustic Signal Processing
- Deep Learning for Underwater Acoustics
- Machine Learning for Environmental Monitoring
Background:
- Ship radiated noise (SRN) recognition is difficult due to background noise and wide signal frequencies.
- Current deep learning models are computationally intensive and use unsuitable RGB channels for SRN data.
Purpose of the Study:
- To develop an optimized neural network for efficient and accurate SRN classification.
- To improve spectral representation and reduce computational load in SRN recognition.
Main Methods:
- Proposed FCResNet5, a streamlined neural network architecture.
- Implemented frequency channelization to enhance spectral representation.
- Focused on critical frequency bands relevant to SRN.
Main Results:
- FCResNet5 achieves comparable accuracy to existing models with greater computational efficiency.
- Ablation studies validated the effectiveness of individual model components.
- Comparative analysis confirmed FCResNet5 as a superior alternative.
Conclusions:
- FCResNet5 provides an efficient and effective solution for ship radiated noise classification.
- The model's design addresses limitations of current deep learning approaches in underwater acoustics.
- Optimized spectral representation and streamlined architecture are key to performance.
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