Morphological Filtering Enhanced Empirical Wavelet Transform for Mode Decomposition
Empirical wavelet transform (EWT) has been successfully utilized for decomposing multi-component signals into intrinsic mode functions. However, it suffers from the spectrum subdividing problem when signals contain non-stationary components which overlap in both the time and frequency domains. In th...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8611084/ |
| _version_ | 1831813797608685568 |
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| author | Biao Xue Hong Hong Songzhao Zhou Gu Chen Yusheng Li Zhiyong Wang Xiaohua Zhu |
| author_facet | Biao Xue Hong Hong Songzhao Zhou Gu Chen Yusheng Li Zhiyong Wang Xiaohua Zhu |
| author_sort | Biao Xue |
| collection | DOAJ |
| description | Empirical wavelet transform (EWT) has been successfully utilized for decomposing multi-component signals into intrinsic mode functions. However, it suffers from the spectrum subdividing problem when signals contain non-stationary components which overlap in both the time and frequency domains. In this paper, a morphological filtering enhanced empirical wavelet transform (EEWT) methodology is presented for mode decomposition of non-stationary signals. Instead of dividing spectrum in terms of the local maxima-minima segmentation principle, the proposed scheme will smooth the spectrum spikes of a signal with morphological filtering so as to keep different intrinsic mode functions in the corresponding spectrum segments. The proposed method is compared to the classical EWT and the EEWT. The experimental results demonstrate that the proposed method is able to achieve better performance of spectrum segmentation and higher resistance to noise than the EWT and EEWT techniques for both synthetic and speech signals. |
| first_indexed | 2024-12-22T22:01:47Z |
| format | Article |
| id | doaj.art-f7fdd9e9a8f24f70bdedffe670410652 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T22:01:47Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f7fdd9e9a8f24f70bdedffe6704106522022-12-21T18:11:07ZengIEEEIEEE Access2169-35362019-01-017142831429310.1109/ACCESS.2019.28927648611084Morphological Filtering Enhanced Empirical Wavelet Transform for Mode DecompositionBiao Xue0Hong Hong1https://orcid.org/0000-0002-1528-8479Songzhao Zhou2Gu Chen3Yusheng Li4Zhiyong Wang5Xiaohua Zhu6School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, ChinaSchool of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, ChinaCollege of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, ChinaSchool of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, ChinaSchool of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, ChinaSchool of Information Technologies, The University of Sydney, Sydney, NSW, AustraliaSchool of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, ChinaEmpirical wavelet transform (EWT) has been successfully utilized for decomposing multi-component signals into intrinsic mode functions. However, it suffers from the spectrum subdividing problem when signals contain non-stationary components which overlap in both the time and frequency domains. In this paper, a morphological filtering enhanced empirical wavelet transform (EEWT) methodology is presented for mode decomposition of non-stationary signals. Instead of dividing spectrum in terms of the local maxima-minima segmentation principle, the proposed scheme will smooth the spectrum spikes of a signal with morphological filtering so as to keep different intrinsic mode functions in the corresponding spectrum segments. The proposed method is compared to the classical EWT and the EEWT. The experimental results demonstrate that the proposed method is able to achieve better performance of spectrum segmentation and higher resistance to noise than the EWT and EEWT techniques for both synthetic and speech signals.https://ieeexplore.ieee.org/document/8611084/Morphological operationswavelet transformsspectral analysis |
| spellingShingle | Biao Xue Hong Hong Songzhao Zhou Gu Chen Yusheng Li Zhiyong Wang Xiaohua Zhu Morphological Filtering Enhanced Empirical Wavelet Transform for Mode Decomposition IEEE Access Morphological operations wavelet transforms spectral analysis |
| title | Morphological Filtering Enhanced Empirical Wavelet Transform for Mode Decomposition |
| title_full | Morphological Filtering Enhanced Empirical Wavelet Transform for Mode Decomposition |
| title_fullStr | Morphological Filtering Enhanced Empirical Wavelet Transform for Mode Decomposition |
| title_full_unstemmed | Morphological Filtering Enhanced Empirical Wavelet Transform for Mode Decomposition |
| title_short | Morphological Filtering Enhanced Empirical Wavelet Transform for Mode Decomposition |
| title_sort | morphological filtering enhanced empirical wavelet transform for mode decomposition |
| topic | Morphological operations wavelet transforms spectral analysis |
| url | https://ieeexplore.ieee.org/document/8611084/ |
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