Passive Underwater Acoustic Signal Separation based on Feature Decoupling Dual-path Network
This addresses the need for tailored signal separation methods in underwater acoustics, which is an incremental improvement over existing speech-derived networks.
The study tackled the problem of separating ship radiated noise in passive underwater acoustic signals by introducing a temporal network with a dual-path model and feature decoupling, achieving effective performance on ShipsEar and DeepShip datasets.
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.