针对水下声学特点设计新型分离网络，有效提升舰船辐射噪声分离效果。
Passive Underwater Acoustic Signal Separation based on Feature Decoupling Dual-path Network
- 采用双路径结构与特征解耦机制，增强信号独立性。
- 在ShipsEar和DeepShip数据集上显著优于现有模型。
- 适合水下声学信号处理、海洋监测等场景使用。
被动水下声学信号分离长期以来依赖深度学习技术分离舰船辐射噪声。然而，当前常用分离网络多源于语音分离应用，未充分考虑水下声学的特殊性，如传播介质差异、信号频率及调制特性等因素。本文提出一种新型时序网络，通过双路径结构与特征解耦方法，将混合信号特征映射至更具独立性的空间，各维度重要性被解耦。随后在分离层融合局部与全局注意力机制。大量对比实验表明，该方法在ShipsEar和DeepShip数据集上表现优异，显著优于其他主流网络模型。
原文摘要 · Abstract (English)
Signal separation in the passive underwater acoustic domain has heavily relied on deep learning techniques to isolate ship radiated noise. However, the separation networks commonly used in this domain stem from speech separation applications and may not fully consider the unique aspects of underwater acoustics beforehand, such as the influence of different propagation media, signal frequencies and modulation characteristics. This oversight highlights the need for tailored approaches that account for the specific characteristics of underwater sound propagation. This study introduces a novel temporal network designed to separate ship radiated noise by employing a dual-path model and a feature decoupling approach. The mixed signals' features are transformed into a space where they exhibit greater independence, with each dimension's significance decoupled. Subsequently, a fusion of local and global attention mechanisms is employed in the separation layer. Extensive comparisons showcase the effectiveness of this method when compared to other prevalent network models, as evidenced by its performance in the ShipsEar and DeepShip datasets.
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