Adversarial Representation Learning for Underwater Acoustic Noise Suppression in LOFARgram Space

Underwater acoustic signal denoising technology aims to overcome the challenge of recovering tonal patterns of target sources such as ships and submarines in complex ocean environments. Conventional statistical methods and deep learning approaches suffer from significant performance degradation under low signal-to-noise ratio (SNR) conditions, and their practical application is further limited by the scarcity of labeled training data. In this paper, we propose an adversarial representation learning framework based on the LOFARgram representation to suppress underwater acoustic noise while preserving tonal structures. To tackle the limited training data problem, we generate simulation data by modeling vessel acoustics and environmental noise based on Wenz curves, augmented with the ShipsEar dataset to improve generalization to real-world conditions. For tonal pattern preservation, we adopt an adversarial learning scheme with an metric-based discriminator to explicitly maintain structural consistency in reconstructed LOFARgrams. To validate the robustness of the proposed method, experiments are conducted on U-Net variants and SE-Mamba architectures with distinct structural properties. Experimental results demonstrate that the proposed method substantially outperforms baseline configurations across various architectures. Qualitative evaluation on real-world recordings suggests practical applicability to operational underwater acoustic denoising.