View in EDS PDF Full Text

SCALABLE COMPUTATIONAL TECHNIQUES FOR PERFORMANCE MOVEMENT ANALYSIS OF MUSICIANS THROUGH IMAGE PROCESSING.

Saved in:
Bibliographic Details
Title: SCALABLE COMPUTATIONAL TECHNIQUES FOR PERFORMANCE MOVEMENT ANALYSIS OF MUSICIANS THROUGH IMAGE PROCESSING.
Authors: WEILONG TAN1 takitan131@163.com
Source: Scalable Computing: Practice & Experience. Jul2025, Vol. 26 Issue 4, p1716-1726. 11p.
Subjects: Image analysis, Camera movement, Musical performance, Cameras, Musicians
Abstract: This study may increase performance by offering insider perspectives on implementation. Musical creativity is limited by traditional movement analysis. Two of these drawbacks are slow feedback and poor accuracy in recording minor motions. Traditional performance analysis has drawbacks, including the inability to record minor activities, subjective interpretations, and reduced accuracy. However, it cannot provide exact insights that increase performance and operational efficiency. These issues may be addressed using scalable Image Processing-based Musician Movements (IP-MM). High-resolution cameras and strong image processing algorithms allow this approach to observe and evaluate artists’ movements. IP-MM provides musicians with quick movement style feedback. IP-MM recognized data trends to enhance strategies and performance. This technique greatly improves movement analysis and gives gamers immediate and meaningful feedback. Improving performance demands prioritizing practice. System performance analysis has improved in IP-MM. As a powerful instrument, it lets musicians push their skills. The new technique improves the performance ratio by 97.2%, the practice efficiency ratio by 98.2%, and the movement patterns ratio by 96.32%. [ABSTRACT FROM AUTHOR]
Copyright of Scalable Computing: Practice & Experience is the property of Scalable Computing: Practice & Experience and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Links:
  – Type: pdflink
Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 185633750
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: SCALABLE COMPUTATIONAL TECHNIQUES FOR PERFORMANCE MOVEMENT ANALYSIS OF MUSICIANS THROUGH IMAGE PROCESSING.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22WEILONG+TAN%22">WEILONG TAN</searchLink><relatesTo>1</relatesTo><i> takitan131@163.com</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Scalable+Computing%3A+Practice+%26+Experience%22">Scalable Computing: Practice & Experience</searchLink>. Jul2025, Vol. 26 Issue 4, p1716-1726. 11p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Image+analysis%22">Image analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Camera+movement%22">Camera movement</searchLink><br /><searchLink fieldCode="DE" term="%22Musical+performance%22">Musical performance</searchLink><br /><searchLink fieldCode="DE" term="%22Cameras%22">Cameras</searchLink><br /><searchLink fieldCode="DE" term="%22Musicians%22">Musicians</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This study may increase performance by offering insider perspectives on implementation. Musical creativity is limited by traditional movement analysis. Two of these drawbacks are slow feedback and poor accuracy in recording minor motions. Traditional performance analysis has drawbacks, including the inability to record minor activities, subjective interpretations, and reduced accuracy. However, it cannot provide exact insights that increase performance and operational efficiency. These issues may be addressed using scalable Image Processing-based Musician Movements (IP-MM). High-resolution cameras and strong image processing algorithms allow this approach to observe and evaluate artists’ movements. IP-MM provides musicians with quick movement style feedback. IP-MM recognized data trends to enhance strategies and performance. This technique greatly improves movement analysis and gives gamers immediate and meaningful feedback. Improving performance demands prioritizing practice. System performance analysis has improved in IP-MM. As a powerful instrument, it lets musicians push their skills. The new technique improves the performance ratio by 97.2%, the practice efficiency ratio by 98.2%, and the movement patterns ratio by 96.32%. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Scalable Computing: Practice & Experience is the property of Scalable Computing: Practice & Experience and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=185633750
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.12694/scpe.v26i4.4665
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 11
        StartPage: 1716
    Subjects:
      – SubjectFull: Image analysis
        Type: general
      – SubjectFull: Camera movement
        Type: general
      – SubjectFull: Musical performance
        Type: general
      – SubjectFull: Cameras
        Type: general
      – SubjectFull: Musicians
        Type: general
    Titles:
      – TitleFull: SCALABLE COMPUTATIONAL TECHNIQUES FOR PERFORMANCE MOVEMENT ANALYSIS OF MUSICIANS THROUGH IMAGE PROCESSING.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: WEILONG TAN
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 07
              Text: Jul2025
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 18951767
          Numbering:
            – Type: volume
              Value: 26
            – Type: issue
              Value: 4
          Titles:
            – TitleFull: Scalable Computing: Practice & Experience
              Type: main
ResultId 1