Underwater reverberation suppression based on non-negative matrix factorisation

Jia, Hongjian; Li, Xiukun

doi:10.1016/j.jsv.2021.116166

Cited by 10 publications

(12 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For ease of use, we have considered the hydrophone on the right end of Figure 1 as the point of origin in the Cartesian and cylindrical coordinates. The displacement caused by the propagating source is time-harmonic, governed by Helmholtz law, and is given as [6,21,17,22,23,24] Figure 1: Model description…”

Section: Underwater Acoustic Propagationmentioning

confidence: 99%

See 1 more Smart Citation

Localization of Impulsive Sound Source in ShallowWaters using a Selective Modal Analysis Algorithm

Talebpour¹,

Mozaffari²,

Saif³

et al. 2023

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

Passive remote monitoring applications of underwater signal processing in a shallow water environment are an impactful area of research for environmental and marine-life monitoring. The majority of the sound source localization techniques require carefully placed synchronized hydrophone arrays, which can be complicated and hard to maintain. In this paper, we utilized the modal dispersions of a signal to derive a localization method for a noisy, shallow water environment. Our proposed algorithm employs modal selection to process the most noiseresistive dispersion curves, improving the accuracy and noise-resistivity of the existing methods. Moreover, we proposed a 2D localization method with multiple unsynchronized hydrophones and minimal hardware requirements and limitations. Furthermore, we analyzed the effects of underwater ambient noise on the accuracy of the proposed method, using simulated and real recorded explosion and whale sounds, and compared our algorithm's localization performance with others. Simulation results show increased localization accuracy of 30m for the recorded explosion sound and 360m for the Whale sound.

show abstract

Section: Underwater Acoustic Propagationmentioning

confidence: 99%

“…Where τ n is the estimated dispersion curve for mode n transmitted over the range R with seabed sound speed c and group velocity υ g ( f, n). To localize the signal, we are looking for a range r that minimizes the statement (21). In other words…”

Section: Localization Algorithmmentioning

confidence: 99%

Localization of Impulsive Sound Source in ShallowWaters using a Selective Modal Analysis Algorithm

Talebpour¹,

Mozaffari²,

Saif³

et al. 2023

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

show abstract

“…Kim et al [23,24] proposed two preprocessing methods that facilitate the application of NMF methods. Jia et al [25] proposed an NMF-based reverberation suppression method that uses matrix rotation for low-rank preprocessing. Even so, reverberation suppression remains a challenging problem in underwater active sonar detection, especially for moving sonar platforms.…”

Section: Introductionmentioning

confidence: 99%

Underwater Reverberation Suppression via Attention and Cepstrum Analysis-Guided Network

Hao

Wang

et al. 2023

JMSE

View full text Add to dashboard Cite

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, and locate underwater targets. Reverberation is the primary background interference for active sonar systems, especially in shallow sea environments. It is coupled with the target echo signal in both the time and frequency domain, which significantly complicates the extraction and analysis of the target echo signal. To combat the effect of reverberation, an attention and cepstrum analysis-guided network (ACANet) is proposed. The baseline system of the ACANet consists of a one-dimensional (1D) convolutional module and a reconstruction module. These are used to perform nonlinear mapping and to reconstruct clean spectrograms, respectively. Then, since most underwater targets contain multiple highlights, a cepstrum analysis module and a multi-head self-attention module are deployed before the baseline system to improve the reverberation suppression performance for multi-highlight targets. The systematic evaluation demonstrates that the proposed algorithm effectively suppresses the reverberation in observed signals and greatly preserves the highlight structure. Compared with NMF methods, the proposed ACANet no longer requires the target echo signal to be low-rank. Thus, it can better suppress the reverberation in multi-highlight observed signals. Furthermore, it demonstrates superior performance over NMF methods in the task of reverberation suppression for single-highlight observed signals. It creates favorable conditions for underwater platforms, such as unmanned underwater vehicles (UUVs), to carry out underwater target detection and tracking tasks.

show abstract

“…In active sonar detection of underwater targets, the targets are often in a proud or buried state, which means that the target echo will be accompanied by solid reverberation interference at the receiving end of the sonar, which seriously affects the detection of underwater targets [ 1 ]. Reverberation is one of the most critical factors affecting sonar detection performance, especially in shallow seas.…”

Section: Introductionmentioning

confidence: 99%

A construction method of reverberation suppression filter using an end-to-end network

Wang,

Zhang,

Chen

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

Reverberation is the primary background interference of active sonar systems in shallow water environments, affecting target position detection accuracy. Reverberation suppression is a signal processing technique used to improve the clarity and accuracy of echo by eliminating the echoes, reverberations, and noise that occur during underwater propagation.This paper proposes an end-to-end network structure called the Reverberation Suppression Network (RS-U-Net) to suppress the reverberation of underwater echo signals. The proposed method effectively improves the signal-to-reverberation ratio (SRR) of the echo signal, outperforming existing methods in the literature. The RS-U-Net architecture uses sonar echo signal data as input, and a one-dimensional convolutional network (1D-CNN) is used in the network to train and extract signal features to learn the main features. The algorithm’s effectiveness is verified by the pool experiment echo data, which shows that the filter can improve the detection of echo signals by about 10 dB. The weights of reverberation suppression tasks are initialized with an auto-encoder, which effectively uses the training time and improves performance. By comparing with the experimental pool data, it is found that the proposed method can improve the reverberation suppression by about 2 dB compared with other excellent methods.

show abstract

Underwater reverberation suppression based on non-negative matrix factorisation

Cited by 10 publications

References 11 publications

Localization of Impulsive Sound Source in ShallowWaters using a Selective Modal Analysis Algorithm

Localization of Impulsive Sound Source in ShallowWaters using a Selective Modal Analysis Algorithm

Underwater Reverberation Suppression via Attention and Cepstrum Analysis-Guided Network

A construction method of reverberation suppression filter using an end-to-end network

Contact Info

Product

Resources

About