Underwater Reverberation Suppression via Attention and Cepstrum Analysis-Guided Network
نویسندگان
چکیده
Active sonar systems are one of the most commonly used acoustic devices for underwater equipment. They use observed signals, which mainly include target echo signals and reverberation, to detect, track, locate targets. Reverberation is primary background interference active systems, especially in shallow sea environments. It coupled with signal both time frequency domain, significantly complicates extraction analysis signal. To combat effect an attention cepstrum analysis-guided network (ACANet) proposed. The baseline system ACANet consists a one-dimensional (1D) convolutional module reconstruction module. These perform nonlinear mapping reconstruct clean spectrograms, respectively. Then, since targets contain multiple highlights, multi-head self-attention deployed before improve reverberation suppression performance multi-highlight systematic evaluation demonstrates that proposed algorithm effectively suppresses greatly preserves highlight structure. Compared NMF methods, no longer requires be low-rank. Thus, it can better suppress signals. Furthermore, superior over methods task single-highlight creates favorable conditions platforms, such as unmanned vehicles (UUVs), carry out detection tracking tasks.
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ژورنال
عنوان ژورنال: Journal of Marine Science and Engineering
سال: 2023
ISSN: ['2077-1312']
DOI: https://doi.org/10.3390/jmse11020313