Multimodal Sparse Time–Frequency Representation for Underwater Acoustic Signals

Miao, Yongchun; Li, Jianghui; Sun, Haixin

doi:10.1109/joe.2020.2987674

Cited by 6 publications

(5 citation statements)

References 39 publications

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“…In the simulation, symmetric α -stable (S α S) distributions is used to model the real-world noise since it represents a different noise statistical character by changing parameters. 13–16 Additionally, the average relative error of the estimated instantaneous frequency φ ^ [ m ] with respect to the true instantaneous frequency is defined as follows…”

Section: Numerical Simulationsmentioning

confidence: 99%

“…The parameterized model needs to be estimated from conventional TFA, so it is needed the initialized TFA with a fair energy concentration. 10–17…”

Section: Introductionmentioning

confidence: 99%

“…The parameterized model needs to be estimated from conventional TFA, so it is needed the initialized TFA with a fair energy concentration. [10][11][12][13][14][15][16][17] Although these advanced TFA technologies can provide more precise insights into the complex structure of a signal, their inherent limitations cannot be ignored, for example, the accuracy is disturbed by timefrequency cells and outliers. [18][19][20][21][22] Minimum ' 1 -norm optimization model has found extensive applications in linear parameter estimations.…”

Section: Introductionmentioning

confidence: 99%

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Accurate time-frequency estimation in sαs noise via memory-dependent derivative

Huang,

Xiao,

Sun

et al. 2023

Advances in Mechanical Engineering

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This letter presents a time-frequency estimation approach based on memory-dependent derivative to obtain accurate spectrograph interpolation information. The memory correlation derivative is the convolution of a time-varying signal with a dynamic weighting function over a past time period with respect to a common derivative. Considering the described method, discrete data from previous times can be derived to estimate the signal values at the current time and to reduce the effect of noise. Fourier transforms with different scales and delay transforms are used as kernel functions to obtain energy-concentrated time-frequency curves with higher resolution and without frequency leakage. Besides, the memory-dependent derivative with adjustable scale factor is used to overcome time-frequency grid mismatches. Furthermore, differing from the phase accumulation manner of conventional time-frequency estimation, ℓ1-norm suppresses the heavy-tailed effect from outliers, thus the robustness of estimator can be enhanced greatly. By suitably choices of scale factor, the estimator can be tuned to exhibit high resolution in targeted regions of the time-frequency spectrum.

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Section: Numerical Simulationsmentioning

confidence: 99%

“…The parameterized model needs to be estimated from conventional TFA, so it is needed the initialized TFA with a fair energy concentration. 10–17…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Accurate time-frequency estimation in sαs noise via memory-dependent derivative

Huang,

Xiao,

Sun

et al. 2023

Advances in Mechanical Engineering

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“…Zhu et al [4] improved network performance by analyzing the spectral components of ship-radiated noise through the extraction of different frequency band spectral features. Other features include wavelet decomposition [5][6][7] and sparse time-frequency representation [8,9].…”

Section: Introductionmentioning

confidence: 99%

Cross-Domain Contrastive Learning-Based Few-Shot Underwater Acoustic Target Recognition

Cui,

He,

Xue

et al. 2024

JMSE

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Underwater Acoustic Target Recognition (UATR) plays a crucial role in underwater detection devices. However, due to the difficulty and high cost of collecting data in the underwater environment, UATR still faces the problem of small datasets. Few-shot learning (FSL) addresses this challenge through techniques such as Siamese networks and prototypical networks. However, it also suffers from the issue of overfitting, which leads to catastrophic forgetting and performance degradation. Current underwater FSL methods primarily focus on mining similar information within sample pairs, ignoring the unique features of ship radiation noise. This study proposes a novel cross-domain contrastive learning-based few-shot (CDCF) method for UATR to alleviate overfitting issues. This approach leverages self-supervised training on both source and target domains to facilitate rapid adaptation to the target domain. Additionally, a base contrastive module is introduced. Positive and negative sample pairs are generated through data augmentation, and the similarity in the corresponding frequency bands of feature embedding is utilized to learn fine-grained features of ship radiation noise, thereby expanding the scope of knowledge in the source domain. We evaluate the performance of CDCF in diverse scenarios on ShipsEar and DeepShip datasets. The experimental results indicate that in cross-domain environments, the model achieves accuracy rates of 56.71%, 73.02%, and 76.93% for 1-shot, 3-shot, and 5-shot scenarios, respectively, outperforming other FSL methods. Moreover, the model demonstrates outstanding performance in noisy environments.

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“…At present, using acoustic waves is the only method for detecting over long distances in seawater (Zhang et al, 2020;Miao et al, 2021a;Miao et al, 2021b). Underwater acoustic technology has become indispensable ocean observation, exploration, and exploitation (Xing et al, 2021;Zhang et al, 2021;Rong and Xu, 2022).…”

Section: Introductionmentioning

confidence: 99%

Inversion of shallow seabed structure and geoacoustic parameters with waveguide characteristic impedance based on Bayesian approach

Zhu

Xue²,

Ren³

et al. 2023

Front. Mar. Sci.

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Underwater acoustic technology is essential for ocean observation, exploration and exploitation, and its development is based on an accurate predication of underwater acoustic wave propagation. In shallow sea environments, the geoacoustic parameters, such as the seabed structure, the sound speeds, the densities, and the sound speed attenuations in seabed layers, would significantly affect the acoustic wave propagation characteristics. To obtain more accurate inversion results for these parameters, this study presents an inversion method using the waveguide characteristic impedance based on the Bayesian approach. In the inversion, the vertical waveguide characteristic impedance, which is the ratio of the pressure over the vertical particle velocity, is set as the matching object. The nonlinear Bayesian theory is used to invert the above geoacoustic parameters and analysis the uncertainty of the inversion results. The numerical studies and the sea experiment processing haven shown the validity of this inversion method. The numerical studies also proved that the vertical waveguide characteristic impedance is more sensitive to the geoacoustic parameters than that of single acoustic pressure or single vertical particle velocity, and the error of simulation inversion is within 3%. The sea experiment processing showed that the seabed layered structure and geoacoustic parameters can be accurately determined by this method. The root mean square between the vertical waveguide characteristic impedance and the measured impedance is 0.38dB, and the inversion results accurately represent the seabed characteristics in the experimental sea area.

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Multimodal Sparse Time–Frequency Representation for Underwater Acoustic Signals

Cited by 6 publications

References 39 publications

Accurate time-frequency estimation in sαs noise via memory-dependent derivative

Accurate time-frequency estimation in sαs noise via memory-dependent derivative

Cross-Domain Contrastive Learning-Based Few-Shot Underwater Acoustic Target Recognition

Inversion of shallow seabed structure and geoacoustic parameters with waveguide characteristic impedance based on Bayesian approach

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