Passive detection and recognition capabilities of Unmanned Underwater Vehicles (UUVs) are significantly degraded by propulsion system self-noise, characterized by pronounced modulation interference and low signal-to-noise ratios. Existing denoising methods commonly produce spectral holes becse of over-suppression and insufficiently mitigate modulation interference. To overcome these limitations, this paper proposes a two-stage denoising-inpainting framework. In the first stage, a mask-based denoising network rapidly attenuates prominent self-noise to obtain a preliminarily enhanced signal. In the second stage, the Spectrum Inpainting Network (SINet) is introduced to precisely reconstruct the target spectrogram. To restore spectral holes and suppress modulation interference, SINet integrates a Modulation-Hole Restoration module to better capture modulation and contextual information. Furthermore, the framework incorporates a Shaft-Frequency Suppression Loss to guide the network focusing toward residual components within the shaft-frequency band in the detection of envelope modulation on noise spectrum. Extensive experiments on the ShipsEar dataset and collected UUV self-noise data demonstrate that the proposed framework can effectively suppress modulation interference and enhance target signal fidelity. The interference shaft-frequency peak-to-average ratio and spectral mean squared error are reduced by 75% and 22%, leading to a notable 6.87% improvement in target recognition accuracy.
Zhao et al. (Sun,) studied this question.