子网
降噪
计算机科学
噪音(视频)
水下
人工智能
信号(编程语言)
语音识别
噪声测量
深度学习
模式识别(心理学)
计算机网络
海洋学
图像(数学)
程序设计语言
地质学
作者
Aolong Zhou,Wen Zhang,Xiaoyong Li,Guili Xu,Bingbing Zhang,Yanxin Ma,Jun Song
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:61: 1-13
被引量:2
标识
DOI:10.1109/tgrs.2023.3254652
摘要
Underwater acoustic signal denoising technology aims to overcome the challenge of recovering valuable ship target signals from noisy audios by suppressing underwater background noise. Traditional statistical-based denoising techniques are difficult to be applied effectively in complex underwater environments, especially in the case of extremely low signal-to-noise ratios (SNRs). To address these problems, we propose a noise-aware deep learning model with fullband-subband attention network (NAFSA-Net) for underwater acoustic signal denoising. NAFSA-Net adopts an encoder to extract the feature representation of the input audio. Subsequently, the noise subnet and the target subnet are designed to estimate the noise component and the target component simultaneously. Specifically, some stacked fullband-subband attention (FSA) blocks are deployed in each subnet to capture both global dependencies and fine-grained local dependencies of features. Furthermore, we introduce an interaction module to transmit auxiliary information from the noise subnet to the target subnet. Finally, we propose an improved weight SI-SNR loss function to optimize the training of our model. Experimental results show that our proposed NAFSA-Net substantially outperforms traditional methods and competitive DNN-based solutions in denoising underwater noisy signals with very low SNRs. More importantly, our proposals achieve equally excellent performance on both unseen datasets, which indicates that NAFSA-Net can be a more robust choice for real-world underwater acoustic denoising systems.
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