Method of Underwater Acoustic Signal Denoising Based on Dual-Path Transformer Network

• Published in: IEEE access Vol. 12; pp. 81483 - 81494
• Main Authors: Song, Yongqiang, Liu, Feng, Shen, Tongsheng
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acoustics; Algorithms; Computational modeling; Convolutional neural networks; Deep learning; dual-path transformer network; Feature extraction; Neural networks; Noise reduction; Scale (ratio); Signal to noise ratio; Tensors; Transformers; Underwater acoustics
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3224752

The presence of natural ambient noise interferes with the system for locating and identifying underwater targets. This paper suggests that a Dual-Path Transformation Network (DPTN) reduces ambient noise in underwater acoustic signals. First, the input acoustic signals' higher-order non-linear features are extracted using a multi-scale convolutional encoder neural network. Second, sub-vectors with the same length are created according to the time dimension from the higher-order non-linear features. The sub-vectors are stitched together to form a three-dimensional tensor. Third, a neural network transformer based on the feed-forward network is constructed. Further, to capture long-term series features and separate the target signal from the noisy signals, the three-dimensional tensor is used as the input of the transformer-based masking network. Finally, overlap-add and transpose are used to obtain discernible target signals. The experimental results verify the effectiveness of the proposed underwater acoustic signal denoising algorithm and demonstrate that the proposed DPRN method can obtain higher output signal-to-noise ratio (SNR) and the scale-invariant signal-to-noise ratio (SI-SNR) compared with the other classical algorithms.