Underwater Acoustic Signal Noise Reduction Based on a Fully Convolutional Encoder-Decoder Neural Network  

作　　者：SONG Yongqiang CHU Qian LIU Feng WANG Tao SHEN Tongsheng 

机构地区：[1]PLA Academy of Military Science,Beijing 100089,China [2]PLA National Innovation Institute of Defense Technology,Beijing 100071,China [3]Yantai Urban and Rural Construction School,Yantai 264000,China

出　　处：《Journal of Ocean University of China》2023年第6期1487-1496,共10页中国海洋大学学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.41906169);the PLA Academy of Military Sciences.

摘　　要：Noise reduction analysis of signals is essential for modern underwater acoustic detection systems.The traditional noise reduction techniques gradually lose efficacy because the target signal is masked by biological and natural noise in the marine environ-ment.The feature extraction method combining time-frequency spectrograms and deep learning can effectively achieve the separation of noise and target signals.A fully convolutional encoder-decoder neural network(FCEDN)is proposed to address the issue of noise reduc-tion in underwater acoustic signals.The time-domain waveform map of underwater acoustic signals is converted into a wavelet low-frequency analysis recording spectrogram during the denoising process to preserve as many underwater acoustic signal characteristics as possible.The FCEDN is built to learn the spectrogram mapping between noise and target signals that can be learned at each time level.The transposed convolution transforms are introduced,which can transform the spectrogram features of the signals into listenable audio files.After evaluating the systems on the ShipsEar Dataset,the proposed method can increase SNR and SI-SNR by 10.02 and 9.5dB,re-spectively.

关 键 词：deep learning convolutional encoder-decoder neural network wavelet low-frequency analysis recording spectrogram 

分 类 号：TB56[交通运输工程—水声工程] TB535[理学—物理] TP18[理学—声学]