Ship Radiated Noise Recognition Using Resonance-Based Sparse Signal Decomposition
Under the complex oceanic environment, robust and effective feature extraction is the key issue of ship radiated noise recognition. Since traditional feature extraction methods are susceptible to the inevitable environmental noise, the type of vessels, and the speed of ships, the recognition accurac...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2017/6930605 |
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| _version_ | 1832558437975719936 |
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| author | Jiaquan Yan Haixin Sun En Cheng Xiaoyan Kuai Xiaoliang Zhang |
| author_facet | Jiaquan Yan Haixin Sun En Cheng Xiaoyan Kuai Xiaoliang Zhang |
| author_sort | Jiaquan Yan |
| collection | DOAJ |
| description | Under the complex oceanic environment, robust and effective feature extraction is the key issue of ship radiated noise recognition. Since traditional feature extraction methods are susceptible to the inevitable environmental noise, the type of vessels, and the speed of ships, the recognition accuracy will degrade significantly. Hence, we propose a robust time-frequency analysis method which combines resonance-based sparse signal decomposition (RSSD) and Hilbert marginal spectrum (HMS) analysis. First, the observed signals are decomposed into high resonance component, low resonance component, and residual component by RSSD, which is a nonlinear signal analysis method based not on frequency or scale but on resonance. High resonance component is multiple simultaneous sustained oscillations, low resonance component is nonoscillatory transients, and residual component is white Gaussian noises. According to the low-frequency periodic oscillatory characteristic of ship radiated noise, high resonance component is the purified ship radiated noise. RSSD is suited to noise suppression for low-frequency oscillation signals. Second, HMS of high resonance component is extracted by Hilbert-Huang transform (HHT) as the feature vector. Finally, support vector machine (SVM) is adopted as a classifier. Real audio recordings are employed in the experiments under different signal-to-noise ratios (SNRs). The experimental results indicate that the proposed method has a better recognition performance than the traditional method under different SNRs. |
| format | Article |
| id | doaj-art-2c84c3d7909140818b8db6d50d413982 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-2c84c3d7909140818b8db6d50d4139822025-02-03T01:32:17ZengWileyShock and Vibration1070-96221875-92032017-01-01201710.1155/2017/69306056930605Ship Radiated Noise Recognition Using Resonance-Based Sparse Signal DecompositionJiaquan Yan0Haixin Sun1En Cheng2Xiaoyan Kuai3Xiaoliang Zhang4Key Laboratory of Underwater Acoustic Communication and Marine Information Technology, Ministry of Education, Xiamen University, Xiamen 361005, ChinaKey Laboratory of Underwater Acoustic Communication and Marine Information Technology, Ministry of Education, Xiamen University, Xiamen 361005, ChinaKey Laboratory of Underwater Acoustic Communication and Marine Information Technology, Ministry of Education, Xiamen University, Xiamen 361005, ChinaKey Laboratory of Underwater Acoustic Communication and Marine Information Technology, Ministry of Education, Xiamen University, Xiamen 361005, ChinaScience and Technology on Underwater Acoustic Antagonizing Laboratory, Systems Engineering Research Institute of China State Shipbuilding Corporation, Beijing 100036, ChinaUnder the complex oceanic environment, robust and effective feature extraction is the key issue of ship radiated noise recognition. Since traditional feature extraction methods are susceptible to the inevitable environmental noise, the type of vessels, and the speed of ships, the recognition accuracy will degrade significantly. Hence, we propose a robust time-frequency analysis method which combines resonance-based sparse signal decomposition (RSSD) and Hilbert marginal spectrum (HMS) analysis. First, the observed signals are decomposed into high resonance component, low resonance component, and residual component by RSSD, which is a nonlinear signal analysis method based not on frequency or scale but on resonance. High resonance component is multiple simultaneous sustained oscillations, low resonance component is nonoscillatory transients, and residual component is white Gaussian noises. According to the low-frequency periodic oscillatory characteristic of ship radiated noise, high resonance component is the purified ship radiated noise. RSSD is suited to noise suppression for low-frequency oscillation signals. Second, HMS of high resonance component is extracted by Hilbert-Huang transform (HHT) as the feature vector. Finally, support vector machine (SVM) is adopted as a classifier. Real audio recordings are employed in the experiments under different signal-to-noise ratios (SNRs). The experimental results indicate that the proposed method has a better recognition performance than the traditional method under different SNRs.http://dx.doi.org/10.1155/2017/6930605 |
| spellingShingle | Jiaquan Yan Haixin Sun En Cheng Xiaoyan Kuai Xiaoliang Zhang Ship Radiated Noise Recognition Using Resonance-Based Sparse Signal Decomposition Shock and Vibration |
| title | Ship Radiated Noise Recognition Using Resonance-Based Sparse Signal Decomposition |
| title_full | Ship Radiated Noise Recognition Using Resonance-Based Sparse Signal Decomposition |
| title_fullStr | Ship Radiated Noise Recognition Using Resonance-Based Sparse Signal Decomposition |
| title_full_unstemmed | Ship Radiated Noise Recognition Using Resonance-Based Sparse Signal Decomposition |
| title_short | Ship Radiated Noise Recognition Using Resonance-Based Sparse Signal Decomposition |
| title_sort | ship radiated noise recognition using resonance based sparse signal decomposition |
| url | http://dx.doi.org/10.1155/2017/6930605 |
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