LW-MS-LFTFNet: A Lightweight Multi-Scale Network Integrating Low-Frequency Temporal Features for Ship-Radiated Noise Recognition
Year: 2025
Type: article
Abstract: Ship-radiated noise (SRN) recognition is vital for underwater acoustics, with applications in both military and civilian fields. Traditional manual recognition by sonar operators is inefficient and error-prone, motivating the development of automated recognition systems. However, most existing deep learning approaches demand high computational resources, limiting their deployment on resource-constrained edge devices. To overcome this challenge, we propose LW-MS-LFTFNet, a lightweight model informed by time-frequency analysis of SRN that highlights the critical role of low-frequency components. The network integrates a multi-scale depthwise separable convolutional backbone with CBAM attention for efficient spectral representation, along with two LSTM-based modules to capture temporal dependencies in low-frequency bands. Experiments on the DeepShip dataset show that LW-MS-LFTFNet achieves 75.04% accuracy with only 0.85 M parameters, 0.38 GMACs, and 3.27 MB of storage, outperforming representative lightweight architectures. Ablation studies further confirm that low-frequency temporal modules contribute complementary gains, improving accuracy by 2.64% with minimal overhead. Guided by domain-specific priors derived from time-frequency pattern analysis, LW-MS-LFTFNet achieves efficient and accurate SRN recognition with strong potential for edge deployment.
DOI: 10.3390/jmse13112073
Authors: Yu Feng, Zhangxin Chen, Yixuan Chen, Ziqin Xie, Jiale He, Jiachang Li, Houqian Ding, Tao Guo, Kai Chen
Language: English
Subfield: Oceanography
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W4415813146