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

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 environment. The feature extraction method combining...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of Ocean University of China Jg. 22; H. 6; S. 1487 - 1496
Hauptverfasser: Song, Yongqiang, Chu, Qian, Liu, Feng, Wang, Tao, Shen, Tongsheng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Heidelberg Science Press 01.12.2023
Springer Nature B.V
PLA Academy of Military Science,Beijing 100089,China
PLA National Innovation Institute of Defense Technology,Beijing 100071,China%Yantai Urban and Rural Construction School,Yantai 264000,China%PLA Academy of Military Science,Beijing 100089,China
Schlagworte:
Acoustic mapping
Acoustic noise
Acoustics
Audio data
Convolution
Deep learning
Earth and Environmental Science
Earth Sciences
Encoders-Decoders
Feature extraction
Frequency analysis
Machine learning
Marine environment
Meteorology
Neural networks
Noise reduction
Oceanography
Sonar detection
Sound detecting and ranging
Spectrograms
Target masking
Underwater
Underwater acoustics
Waveforms
Wavelet analysis
wavelet low-frequency analysis recording spectrogram
deep learning
convolutional encoder-decoder neural network
ISSN:1672-5182, 1993-5021, 1672-5174
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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 environment. 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 reduction 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, respectively.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1672-5182
1993-5021
1672-5174
DOI:10.1007/s11802-023-5458-z