Academic Journal
View in EDS

A Spectrum Adaptive Segmentation Empirical Wavelet Transform for Noisy and Nonstationary Signal Processing

Bibliographic Details
Title: A Spectrum Adaptive Segmentation Empirical Wavelet Transform for Noisy and Nonstationary Signal Processing
Authors: Bobai Zhao, Qinglong Li, Qian Lv, Xiameng Si
Source: IEEE Access, Vol 9, Pp 106375-106386 (2021)
Publisher Information: IEEE, 2021.
Publication Year: 2021
Collection: LCC:Electrical engineering. Electronics. Nuclear engineering
Subject Terms: Empirical wavelet transform, spectrum segmentation, grayscale morphology, normalized cut, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
More Details: Compared with thresholding methods based on the traditional wavelet transform (WT), empirical wavelet transform (EWT) has been demonstrated to outperform in terms of noise elimination by constructing an adaptive filter bank. However, as the state-of-the-art version of EWT, enhanced EWT (EEWT) requires that the number of components in the superposed signal as prior knowledge is known, which is impractical in reality and limits the application of this method. In this paper, a novel EWT that can adaptively estimate the number of components in the signal and achieve spectrum segmentation is proposed and is referred to as the spectrum adaptive segmentation empirical wavelet transform (SAS-EWT). Furthermore, a customized SAS-EWT for speech enhancement is proposed. According to the experimental results, our proposed SAS-EWT provides more accurate boundary detection and better denoising performance. The proposed method improves the performance by up to 5% in terms of PESQ, STOI, and SNR in comparison to EEWT.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 2169-3536
Relation: https://ieeexplore.ieee.org/document/9494350/; https://doaj.org/toc/2169-3536
DOI: 10.1109/ACCESS.2021.3099500
Access URL: https://doaj.org/article/6e11d3ca1c944467ad59ff025f8cef85
Accession Number: edsdoj.6e11d3ca1c944467ad59ff025f8cef85
Database: Directory of Open Access Journals
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://login.libproxy.scu.edu/login?url=http://ieeexplore.ieee.org/search/searchresult.jsp?action=search&newsearch=true&queryText=%22DOI%22:10.1109/ACCESS.2021.3099500
    Name: EDS - IEEE (s8985755)
    Category: fullText
    Text: Check IEEE Xplore for full text
    MouseOverText: Check IEEE Xplore for full text. A new window will open.
  – Url: https://resolver.ebsco.com/c/xy5jbn/result?sid=EBSCO:edsdoj&genre=article&issn=21693536&ISBN=&volume=9&issue=&date=20210101&spage=106375&pages=106375-106386&title=IEEE Access&atitle=A%20Spectrum%20Adaptive%20Segmentation%20Empirical%20Wavelet%20Transform%20for%20Noisy%20and%20Nonstationary%20Signal%20Processing&aulast=Bobai%20Zhao&id=DOI:10.1109/ACCESS.2021.3099500
    Name: Full Text Finder (for New FTF UI) (s8985755)
    Category: fullText
    Text: Find It @ SCU Libraries
    MouseOverText: Find It @ SCU Libraries
  – Url: https://doaj.org/article/6e11d3ca1c944467ad59ff025f8cef85
    Name: EDS - DOAJ (s8985755)
    Category: fullText
    Text: View record from DOAJ
    MouseOverText: View record from DOAJ
Header DbId: edsdoj
DbLabel: Directory of Open Access Journals
An: edsdoj.6e11d3ca1c944467ad59ff025f8cef85
RelevancyScore: 944
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 943.9462890625
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: A Spectrum Adaptive Segmentation Empirical Wavelet Transform for Noisy and Nonstationary Signal Processing
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Bobai+Zhao%22">Bobai Zhao</searchLink><br /><searchLink fieldCode="AR" term="%22Qinglong+Li%22">Qinglong Li</searchLink><br /><searchLink fieldCode="AR" term="%22Qian+Lv%22">Qian Lv</searchLink><br /><searchLink fieldCode="AR" term="%22Xiameng+Si%22">Xiameng Si</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: IEEE Access, Vol 9, Pp 106375-106386 (2021)
– Name: Publisher
  Label: Publisher Information
  Group: PubInfo
  Data: IEEE, 2021.
– Name: DatePubCY
  Label: Publication Year
  Group: Date
  Data: 2021
– Name: Subset
  Label: Collection
  Group: HoldingsInfo
  Data: LCC:Electrical engineering. Electronics. Nuclear engineering
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Empirical+wavelet+transform%22">Empirical wavelet transform</searchLink><br /><searchLink fieldCode="DE" term="%22spectrum+segmentation%22">spectrum segmentation</searchLink><br /><searchLink fieldCode="DE" term="%22grayscale+morphology%22">grayscale morphology</searchLink><br /><searchLink fieldCode="DE" term="%22normalized+cut%22">normalized cut</searchLink><br /><searchLink fieldCode="DE" term="%22Electrical+engineering%2E+Electronics%2E+Nuclear+engineering%22">Electrical engineering. Electronics. Nuclear engineering</searchLink><br /><searchLink fieldCode="DE" term="%22TK1-9971%22">TK1-9971</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: Compared with thresholding methods based on the traditional wavelet transform (WT), empirical wavelet transform (EWT) has been demonstrated to outperform in terms of noise elimination by constructing an adaptive filter bank. However, as the state-of-the-art version of EWT, enhanced EWT (EEWT) requires that the number of components in the superposed signal as prior knowledge is known, which is impractical in reality and limits the application of this method. In this paper, a novel EWT that can adaptively estimate the number of components in the signal and achieve spectrum segmentation is proposed and is referred to as the spectrum adaptive segmentation empirical wavelet transform (SAS-EWT). Furthermore, a customized SAS-EWT for speech enhancement is proposed. According to the experimental results, our proposed SAS-EWT provides more accurate boundary detection and better denoising performance. The proposed method improves the performance by up to 5% in terms of PESQ, STOI, and SNR in comparison to EEWT.
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: article
– Name: Format
  Label: File Description
  Group: SrcInfo
  Data: electronic resource
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 2169-3536
– Name: NoteTitleSource
  Label: Relation
  Group: SrcInfo
  Data: https://ieeexplore.ieee.org/document/9494350/; https://doaj.org/toc/2169-3536
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1109/ACCESS.2021.3099500
– Name: URL
  Label: Access URL
  Group: URL
  Data: <link linkTarget="URL" linkTerm="https://doaj.org/article/6e11d3ca1c944467ad59ff025f8cef85" linkWindow="_blank">https://doaj.org/article/6e11d3ca1c944467ad59ff025f8cef85</link>
– Name: AN
  Label: Accession Number
  Group: ID
  Data: edsdoj.6e11d3ca1c944467ad59ff025f8cef85
PLink https://login.libproxy.scu.edu/login?url=https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&scope=site&db=edsdoj&AN=edsdoj.6e11d3ca1c944467ad59ff025f8cef85
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1109/ACCESS.2021.3099500
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 12
        StartPage: 106375
    Subjects:
      – SubjectFull: Empirical wavelet transform
        Type: general
      – SubjectFull: spectrum segmentation
        Type: general
      – SubjectFull: grayscale morphology
        Type: general
      – SubjectFull: normalized cut
        Type: general
      – SubjectFull: Electrical engineering. Electronics. Nuclear engineering
        Type: general
      – SubjectFull: TK1-9971
        Type: general
    Titles:
      – TitleFull: A Spectrum Adaptive Segmentation Empirical Wavelet Transform for Noisy and Nonstationary Signal Processing
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Bobai Zhao
      – PersonEntity:
          Name:
            NameFull: Qinglong Li
      – PersonEntity:
          Name:
            NameFull: Qian Lv
      – PersonEntity:
          Name:
            NameFull: Xiameng Si
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2021
          Identifiers:
            – Type: issn-print
              Value: 21693536
          Numbering:
            – Type: volume
              Value: 9
          Titles:
            – TitleFull: IEEE Access
              Type: main
ResultId 1