Ship Radiated Noise Recognition Based on Dual Low-Rank Adaptation Training
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摘要: 针对深度学习模型在船舶辐射噪声识别中由数据短缺导致的泛化能力受限问题, 文中提出了权重-特征双低秩迁移学习框架。该框架从模型权重和特征表达两个维度协同展开低秩优化: 在权重空间, 冻结预训练权重, 通过轻量化低秩权重调整(WLoRA)模块构建可学习低秩权重更新, 以较少参数量完成权重微调, 从而降低过拟合风险; 在特征空间, 基于船舶辐射噪声Mel时频谱的内在低秩结构, 通过低秩特征调整(FLoRA)模块对特征进行压缩和重构, 从而显式约束模型学习低秩特征。该框架充分考虑了Mel时频谱的固有低秩结构, 深入挖掘预训练模型潜力, 有效提升了迁移学习性能。通过ShipsEar和Deepship两个公开数据集的实验证明, 相对于直接微调预训练模型, 所提出方法能够有效提升迁移学习在船舶辐射嗓声分类模型中的性能。进一步的消融实验验证了两个低秩模块的有效性。Abstract: To address the limited generalization capability of deep learning models in ship-radiated noise recognition, this paper proposes a dual low-rank transfer learning framework that simultaneously optimizes both model weights and feature representations. Specifically, in the weight space, the pretrained weights are frozen, and a lightweight low-rank weight adjustment(WLoRA) module is introduced to construct learnable low-rank increments. This strategy enables efficient fine-tuning with significantly fewer trainable parameters, thereby mitigating the risk of overfitting. In the feature space, considering the inherent low-rank properties of Mel spectrograms derived from ship-radiated noise, a low-rank feature adjustment(FLoRA) module is designed to compress and reconstruct the extracted features. This explicit low-rank constraint encourages the model to learn compact and discriminative representations that better capture the essential structures of ship-radiated noise. By jointly exploiting low-rank optimization in both the weight and feature dimensions, the framework maximizes the potential of pretrained models and improves transfer learning performance. The experimental results on two publicly available underwater acoustic datasets, ShipsEar and Deepship, demonstrate that the proposed method significantly enhances the performance of transfer learning in the classification model of ship-radiated noise compared to direct fine-tuning of pre-trained models. Furthermore, ablation studies validate the effectiveness of the two low-rank modules.
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Key words:
- ship radiated noise /
- low rank analysis /
- transfer learning /
- LoRA training
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图 1 低秩权重/特征调整模块与完全微调方法结构图
Figure 1. Structure diagram of WLoRA, FLoRA and complete fine-tuning method
图 6 ShipsEar和DeepShip数据集的混淆矩阵
Figure 6. Confusion matrix of ShipsEar and DeepShip datasets
图 7 ShipsEar和DeepShip特征聚类结果
Figure 7. Feature clustering results of ShipsEar and DeepShip Datasets
图 8 ShipsEar和DeepShip的ROC曲线及AUC指标
Figure 8. ROC curves and AUC value of ShipsEar and DeepShip datasets
表 1 数据集划分
Table 1. Division of datasets.
数据集 训练音频 测试音频 训练样本 测试样本 ShipsEar 64 21 470 117 DeepShip 403 137 7 650 2 516 
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表 2 ShipsEar数据集上对比实验结果
Table 2. Results of experiments on ShipsEar dataset.
方法 特征 OA/% Params/M 时间/s FLOPs/G ResNet18 Mel 79.5 11.179 768 4.40 SIR&LMR Mel 83.8 11.841 1 733 13.40 CMoE STFT 85.5 11.191 2 102 55.62 所提方法 Mel 86.3 11.241 824 4.43
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表 3 DeepShip数据集上对比实验结果
Table 3. Results of experiments on DeepShip dataset.
方法 特征 OA/% Params/M 时间/s FLOPs/G ResNet18 Mel 74.8 11.179 768 4.40 SIR&LMR Mel 77.1 11.841 1 733 17.70 CMoE STFT 76.8 11.191 2 102 16.90 所提方法 Mel 77.2 11.241 824 4.43
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表 4 模块消融实验结果
Table 4. Results of ablation experiments
方法 OA Params ResNet 77.7% 11.179M ResNet-pre 79.5% 11.179M WLoRA 83.8% 11.179M FLoRA 85.5% 11.241M Full 86.3% 11.241M
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表 5
$ \alpha $ 参数敏感性实验Table 5. Results of
$ \boldsymbol{\alpha } $ sensitivity analysis$ \alpha $取值 OA/% Params/M 0.0 82.1 11.241 0.1 83.5 11.241 0.2 86.3 11.241 0.3 85.5 11.241 0.4 82.1 11.241 0.5 80.3 11.241
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表 6
$ {K}_{w} $ 参数敏感性实验Table 6. Results of
$ {\boldsymbol{K}}_{\boldsymbol{w}} $ sensitivity analysis方法 OA/% Params/M 4 82.1 11.241 8 83.8 11.241 16 85.5 11.241 32 86.3 11.241 64 86.3 11.241
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表 7 单独使用WLoRA的
$ {\boldsymbol{K}}_{\boldsymbol{w}} $ 参数敏感性实验Table 7. Results of
$ {\boldsymbol{K}}_{\boldsymbol{w}} $ sensitivity analysis without FLoRA Module方法 OA/% Params/M 4 77.7 11.179 8 80.3 11.179 16 82.1 11.179 32 82.1 11.179 64 83.8 11.179
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表 8
$ {\boldsymbol{K}}_{\boldsymbol{f}} $ 参数敏感性实验Table 8. Results of
$ {\boldsymbol{K}}_{\boldsymbol{f}} $ sensitivity analysis方法 OA/% Params/M 4 83.8 11.187 8 82.1 11.195 16 84.6 11.201 32 86.3 11.241 64 85.5 11.302
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