Ship-Radiated Noise Recognition Based on Dual Low-Rank Adaptation Training

To address the limited generalization capability of deep learning models in ship-radiated noise recognition caused by data shortage and domain shift, this paper proposed a dual low-rank adaptation transfer learning framework of weights and features. This framework conducted low-rank optimization simultaneously from two dimensions: model weights and feature representations. In the weight space, the pretrained weights were frozen, and a lightweight weight low-rank adaptation (WLoRA) module was introduced to construct learnable low-rank weight parameters, completing the weight fine-tuning with fewer parameters and thereby reducing the risk of overfitting. In the feature space, based on the inherent low-rank structure of the Mel spectrogram of ship-radiated noise, the feature was compressed and reconstructed through the low-rank feature adjustment (FLoRA) module, thereby explicitly constraining the model to learn low-rank features. This framework fully took into account the inherent low-rank structure of the Mel spectrogram, deeply explored the potential of pretrained models, and effectively improved the performance of transfer learning. Experimental results on the public datasets ShipsEar and Deepship show that compared with directly fine-tuning the pretrained model, the proposed method can effectively enhance the performance of transfer learning in the classification model of ship-radiated noise. Further ablation experiments have verified the effectiveness of the two low-rank modules.