Enhanced noise resilience in passive tone detection via broad-receptive field complex-valued convolutional neural networks

Abstract

Tone detection is crucial for passive sonar systems. Numerous algorithms have been developed for passive tone detection, but their effectiveness in detecting weak tones is still limited. To enhance noise resilience in passive tone detection, a broad-receptive field complex-valued structure named attention-driven complex-valued U-Net is proposed. Concretely, two attention mechanisms, namely, temporal attention and harmonic attention, are proposed to broaden the receptive field with high computational efficiency. Complex-valued operators are then introduced to mine both amplitude and phase information of tones. Additionally, a symmetric downsampling and upsampling strategy is proposed to improve the reconstruction accuracy of detailed time-frequency information. Overall, the proposed method demonstrates a strong robustness to noise and a strong ability to generalize. Experimental results on both simulated data and real-world data validate the superiority of the proposed attention-driven complex-valued U-Net against conventional U-shaped structures.