Joint Time-Frequency Inversion for Seabed Properties of Ship Noise on a Vertical Line Array in South China Sea

Xu, Liya; Yang, Kunde; Yang, Qi

doi:10.1109/access.2018.2876567

Cited by 6 publications

(4 citation statements)

References 26 publications

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“…In [13], a time-frequency graph was used to evaluate the noise suppression performance of a machine learning model. In [14] and [15], a time-frequency graph and correlation matrix were used to explore the influences of various noise sources around the South China Sea, such as ships, warships, plate movements, and wind, on the sound frequency ranges at sea [16]. The time-frequency diagram was used to compare the frequency-domain difference between an original signal and the same signal with various added noise signals.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning and Prediction of Masked Motors With Different Materials Based on Noise Analysis

Wen

Huang

2022

IEEE Access

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The effect of noise on the human body has attracted increasing research attention. In particular, many factories generate motor noise pollution, which exposes general workers to noise for extended periods. To solve this problem, masks made of different materials are used for reducing the noise generated by motors. In this study, we attempted to predict the acoustic sound of masked motors. We collected noise level data in decibels for different operation frequencies of motors used at National Synchrotron Radiation Research Center (NSRRC) and developed a machine learning model according to the characteristics of the collected data to simulate the effect of masks on the motor sound. We use the Gradient Boost Model (GBM) as the main learning method because the model is suitable for predicting noise from comparison results of the five models are very common predictive models and may performed as compare method to predict acoustic noise. The results indicated that the prediction accuracy of the GBM was considerably higher than other four traditional machine learning methods (random forests, support vector machine, gaussian processes regression model and multiple linear regression models). Moreover, we used a general multiple linear regression method as the worst method of comparison and conducted time-frequency visualization of the sound for analysis. At NSRRC, we examined the effects of three observation locations and three mask materials, namely wood, metal, and acrylic, on the sound prediction accuracy achieved with the developed model. The highest sound prediction accuracy was obtained behind the motor and under an acrylic mask.INDEX TERMS Gradient boosting model (GBM), machine learning, motor noise prediction, timefrequency diagrams.

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Section: Introductionmentioning

confidence: 99%

Machine Learning and Prediction of Masked Motors With Different Materials Based on Noise Analysis

Wen

Huang

2022

IEEE Access

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“…In underwater acoustic applications, the passive source localization/tracking and ocean parameters inversion are always hot issues of serious concerned [1][2][3][4][5]. Matched-field processing (MFP) is a popular approach used for source localization and tracking [6,7], which has been applied with excellent results both to synthetic and real data.…”

Section: Introductionmentioning

confidence: 99%

An Improved Particle Filtering Technique for Source Localization and Sound Speed Field Inversion in Shallow Water

Dai

et al. 2020

IEEE Access

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Both source localization and environmental inversions are practical problems for longstanding applications in underwater acoustics. This paper presents an approach of the moving source localization and sound speed field (SSF) inversion in shallow water. The approach is formulated in a statespace model with a state equation for both the source parameters (e.g., source depth, range, and speed) and SSF parameters (first three empirical orthogonal function coefficients, EOFs) and a measurement equation that incorporates underwater acoustic information via a vertical line array (VLA). As a sequential processing algorithm that operates on nonlinear systems with non-Gaussian probability densities, an improved sequential importance resampling type particle filtering (SIR PF) is proposed to counter degeneracy. The improved PF performs tracking of source and SSF parameters simultaneously, and evaluates their uncertainties in the form of time-evolving posterior probability densities (PPDs). The performance of improved PF is illustrated with well-tracked simulations of real-time source localization and time-varying SSF inversion. Moreover, the influence of different particle numbers on PF tracking accuracy and computational cost is also demonstrated. Simulation results show that the high-particle-number PF has an outperform performance. For a given hardware system, the reasonable compromise between accuracy and computational cost is a matter of tradeoff.

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“…The frequency feature extraction method usually consists of three steps: (1) signal processing, (2) feature extraction and (3) classification, among which the first two steps have a great impact on feature extraction. Therefore, we face two challenges: how to select the right signal processing method and how to extract features accurately [6,7].…”

Section: Introductionmentioning

confidence: 99%

A Fusion Frequency Feature Extraction Method for Underwater Acoustic Signal Based on Variational Mode Decomposition, Duffing Chaotic Oscillator and a Kind of Permutation Entropy

Chen

et al. 2019

Electronics

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In order to effectively extract the frequency characteristics of an underwater acoustic signal under sensor measurement, a fusion frequency feature extraction method for an underwater acoustic signal is presented based on variational mode decomposition (VMD), duffing chaotic oscillator (DCO) and a kind of permutation entropy (PE). Firstly, VMD decomposes the complex multi-component underwater acoustic signal into a set of intrinsic mode functions (IMFs), so as to extract the estimated center frequency of each IMF. Secondly, the frequency of the line spectrum can be obtained by using DCO and a kind of PE (KPE). DCO is used to detect the actual frequency of the line spectrum for each IMF and KPE can determine the accurate frequency when the phase space track is in the great periodic state. Finally, the frequency characteristic parameters acted as the input of the support vector machine (SVM) to distinguish different types of underwater acoustic signals. By comparing with the other three traditional methods for simulation signal and different kinds of underwater acoustic signals, the results show that the proposed method can accurately extract the frequency characteristics and effectively realize the classification and recognition for the underwater acoustic signal.

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Joint Time-Frequency Inversion for Seabed Properties of Ship Noise on a Vertical Line Array in South China Sea

Cited by 6 publications

References 26 publications

Machine Learning and Prediction of Masked Motors With Different Materials Based on Noise Analysis

Machine Learning and Prediction of Masked Motors With Different Materials Based on Noise Analysis

An Improved Particle Filtering Technique for Source Localization and Sound Speed Field Inversion in Shallow Water

A Fusion Frequency Feature Extraction Method for Underwater Acoustic Signal Based on Variational Mode Decomposition, Duffing Chaotic Oscillator and a Kind of Permutation Entropy

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