Pass-by Characterization of Noise Emitted by Different Categories of Seagoing Ships in Ports

Fredianelli, Luca; Nastasi, Marco; Bernardini, Marco; Fidecaro, F.; Licitra, Gaetano

doi:10.3390/su12051740

Cited by 51 publications

(42 citation statements)

References 44 publications

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“…This method can improve the accuracy of sound source localization and recognition when the closed acoustic cavity is used as a carriage [ 9 ]. In the study of noise source identification in a port area, Fredianelli, L. et al described the sound power level and 1/3 octave sound power spectrum of low-speed ships and identified five different types of noise emitted by ocean-going ships [ 10 ]. They also used a multiple regression analysis to analyze the impact of a ship’s minimum distance, speed, and draft on ship noise emissions [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Separation of the Sound Power Spectrum of Multiple Sources by Three-Dimensional Sound Intensity Decomposition

Chai

Liu

Xiong

2021

Sensors

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The identification and separation of sources are the prerequisite of industrial noise control. Industrial machinery usually contains multiple noise sources sharing same-frequency components. There are usually multiple noise sources in mechanical equipment, and there are few effective methods available to separate the spectrum intensity of each sound source. This study tries to solve the problem by the radiation relationship between three-dimensional sound intensity vectors and the power of the sources. When the positions of the probe and the sound source are determined, the sound power of the sound source at each frequency can be solved by the particle swarm optimization algorithm. The solution results at each frequency are combined to obtain the sound power spectrum of each sound source. The proposed method is first verified by a simulation on two point sources. The experiment is carried out on a fault simulation test bed in an ordinary laboratory; we used three three-dimensional sound intensity probes to form a line array and conducted spectrum separation of the nine main noise sources. The sound intensity on the main frequency band of each sound source was close to the result of the near-field measurement of the one-dimensional sound intensity probe. The proposed spectral separation method of the sound power of multiple sound sources provides a new method for accurate noise identification in industrial environments.

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Section: Introductionmentioning

confidence: 99%

Separation of the Sound Power Spectrum of Multiple Sources by Three-Dimensional Sound Intensity Decomposition

Chai

Liu

Xiong

2021

Sensors

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“…The frequency and propagation distance of the ship-radiated noise (SRN) will also affect the nearby residents to different degrees at different times. As a result, in-depth research on various SRN sources can ensure an uninterrupted life of nearby residents and the reproduction of marine life [ 1 , 2 , 3 , 4 ]. The extraction of inherent characteristics of SRN can effectively improve the deficiency of passive sonar and the accuracy of target recognition, making it the most important link in ship classification [ 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction of Ship-Radiated Noise Based on Enhanced Variational Mode Decomposition, Normalized Correlation Coefficient and Permutation Entropy

Xie

Esmaiel

Sun

et al. 2020

Entropy

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Due to the complexity and variability of underwater acoustic channels, ship-radiated noise (SRN) detected using the passive sonar is prone to be distorted. The entropy-based feature extraction method can improve this situation, to some extent. However, it is impractical to directly extract the entropy feature for the detected SRN signals. In addition, the existing conventional methods have a lack of suitable de-noising processing under the presence of marine environmental noise. To this end, this paper proposes a novel feature extraction method based on enhanced variational mode decomposition (EVMD), normalized correlation coefficient (norCC), permutation entropy (PE), and the particle swarm optimization-based support vector machine (PSO-SVM). Firstly, EVMD is utilized to obtain a group of intrinsic mode functions (IMFs) from the SRN signals. The noise-dominant IMFs are then eliminated by a de-noising processing prior to PE calculation. Next, the correlation coefficient between each signal-dominant IMF and the raw signal and PE of each signal-dominant IMF are calculated, respectively. After this, the norCC is used to weigh the corresponding PE and the sum of these weighted PE is considered as the final feature parameter. Finally, the feature vectors are fed into the PSO-SVM multi-class classifier to classify the SRN samples. The experimental results demonstrate that the recognition rate of the proposed methodology is up to 100%, which is much higher than the currently existing methods. Hence, the method proposed in this paper is more suitable for the feature extraction of SRN signals.

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“…Signal analysis of ship-radiated noise (SRN) is important, not only for military operations but also for environmental protection [ 1 , 2 ]. Feature extraction of SRN signals can improve the performance of detection equipment to some extent [ 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

A New Feature Extraction Method Based on Improved Variational Mode Decomposition, Normalized Maximal Information Coefficient and Permutation Entropy for Ship-Radiated Noise

Xie

Sun

2020

Entropy

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Due to the existence of marine environmental noise, coupled with the instability of underwater acoustic channel, ship-radiated noise (SRN) signals detected by sensors tend to suffer noise pollution as well as distortion caused by the transmission medium, making the denoising of the raw detected signals the new focus in the field of underwater acoustic target recognition. In view of this, this paper presents a novel hybrid feature extraction scheme integrating improved variational mode decomposition (IVMD), normalized maximal information coefficient (norMIC) and permutation entropy (PE) for SRN signals. Firstly, the IVMD method is employed to decompose the SRN signals into a number of finite intrinsic mode functions (IMFs). The noise IMFs are then filtered out by a denoising method before PE extraction. Next, the MIC between each retained IMF and the raw SRN signal and PE of retained IMFs are calculated, respectively. After this, the norMICs are used to weigh the PE values of the retained IMFs and the sum of the weighted PE results is regarded as the classification parameter. Finally, the feature vectors are fed into the particle swarm optimization-based support vector machine multi-class classifier (PSO-SVM) to identify different types of SRN samples. The experimental results have indicated that the classification accuracy of the proposed method is as high as 99.1667%, which is much higher than that of other currently existing methods. Hence, the method proposed in this paper is more suitable for feature extraction of SRN signals in practical application.

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Pass-by Characterization of Noise Emitted by Different Categories of Seagoing Ships in Ports

Cited by 51 publications

References 44 publications

Separation of the Sound Power Spectrum of Multiple Sources by Three-Dimensional Sound Intensity Decomposition

Separation of the Sound Power Spectrum of Multiple Sources by Three-Dimensional Sound Intensity Decomposition

Feature Extraction of Ship-Radiated Noise Based on Enhanced Variational Mode Decomposition, Normalized Correlation Coefficient and Permutation Entropy

A New Feature Extraction Method Based on Improved Variational Mode Decomposition, Normalized Maximal Information Coefficient and Permutation Entropy for Ship-Radiated Noise

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