RGDNet: Recognition-Guided Underwater Acoustic Signal Denoising via Mask Integration and Signal Decoupling

This article proposes a novel recognition-guided denoising method for underwater acoustic signals, termed RGDNet, which is designed to bridge the gap where existing methods, despite achieving high denoising metrics, fail to effectively enhance recognition accuracy. Specifically, we first designed a task-specific mask integration (TSMI) module that minimizes phase and frequency distortions by converting signal fusion across tasks into a unified mask-based fusion, ensuring the fusion of signal features remains effective. We further developed a context-aware signal decoupling (CASD) module to maintain the distinctiveness of sub-task signals through segmented context analysis. Following this, a recognition-guided branch (RGB) equipped with an auxiliary classifier is proposed, which guides the optimization of the denoising process based on recognition loss. Additionally, we also implemented a complex domain multimetric discriminator (CDMD) that assesses the quality of denoised signals from both metric-focused and task-specific perspectives, effectively linking denoising metrics with recognition performance. Extensive experiments show that RGDNet significantly outperforms existing methods, increasing the scale-invariant signal-to-noise ratio (SI-SNR) by an average of 6.33 dB and enhancing recognition accuracy by 11.26%.