S6 Data -
<div><p>This study develops an innovative method for analyzing and clustering tonal trends in Chinese Yue Opera to identify different vocal styles accurately. Linear interpolation is applied to process the time series data of vocal melodies, addressing inconsistent feature dimensions. Th...
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| مؤلفون آخرون: | , , , |
| منشور في: |
2025
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| الموضوعات: | |
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| _version_ | 1852023278235811840 |
|---|---|
| author | Yuhang Zhang (3144870) |
| author2 | Xiaofeng Wu (288608) Jiawei Xu (394968) Zihao Ning (20485484) Xiao Han (102316) |
| author2_role | author author author author |
| author_facet | Yuhang Zhang (3144870) Xiaofeng Wu (288608) Jiawei Xu (394968) Zihao Ning (20485484) Xiao Han (102316) |
| author_role | author |
| dc.creator.none.fl_str_mv | Yuhang Zhang (3144870) Xiaofeng Wu (288608) Jiawei Xu (394968) Zihao Ning (20485484) Xiao Han (102316) |
| dc.date.none.fl_str_mv | 2025-01-24T18:57:49Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0313065.s006 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/S6_Data_-/28275716 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Science Policy Plant Biology Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified traditional rhythmic norms time series data surpassing traditional methods simulation results reveal identifying various styles tonal transition redundancies manage data uncertainties classifying vocal styles means </ p improving classification accuracy clustering tonal trends chinese yue opera yue opera tonal ranges eliminate uncertainties vocal patterns vocal melodies clustering method xlink "> thereby enhancing study develops musical tones linear interpolation interdisciplinary research innovative method fuzzy c findings enhance correlation function convergence speed artistic creation approach achieves |
| dc.title.none.fl_str_mv | S6 Data - |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <div><p>This study develops an innovative method for analyzing and clustering tonal trends in Chinese Yue Opera to identify different vocal styles accurately. Linear interpolation is applied to process the time series data of vocal melodies, addressing inconsistent feature dimensions. The second-order difference method extracts tonal trend features. We introduce a fuzzy C-means clustering method enhanced by quantum particle swarm optimization (QPSO) to manage data uncertainties, improving classification accuracy and convergence speed. Additionally, we employ a cross-correlation function to eliminate uncertainties from tonal transition redundancies. We designed a detection algorithm using trend data to validate our clustering method, thereby enhancing the accuracy of the analysis of tonal ranges and potential models. This method detects whether Yue Opera adheres to traditional rhythmic norms and models the regularity of musical tones and vocal patterns. Simulation results reveal that our approach achieves a 91.4% accuracy in classifying vocal styles, surpassing traditional methods and demonstrating its potential for identifying various styles. This research offers technical support for Yue Opera music education and interdisciplinary research. The findings enhance the quality of artistic creation and performance in Yue Opera, ensuring its preservation and development.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_f8f8913cd40f8e348be6f00a0d96d7e1 |
| identifier_str_mv | 10.1371/journal.pone.0313065.s006 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28275716 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | S6 Data - Yuhang Zhang (3144870)Xiaofeng Wu (288608)Jiawei Xu (394968)Zihao Ning (20485484)Xiao Han (102316)Science PolicyPlant BiologySpace ScienceBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedtraditional rhythmic normstime series datasurpassing traditional methodssimulation results revealidentifying various stylestonal transition redundanciesmanage data uncertaintiesclassifying vocal stylesmeans </ pimproving classification accuracyclustering tonal trendschinese yue operayue operatonal rangeseliminate uncertaintiesvocal patternsvocal melodiesclustering methodxlink ">thereby enhancingstudy developsmusical toneslinear interpolationinterdisciplinary researchinnovative methodfuzzy cfindings enhancecorrelation functionconvergence speedartistic creationapproach achieves<div><p>This study develops an innovative method for analyzing and clustering tonal trends in Chinese Yue Opera to identify different vocal styles accurately. Linear interpolation is applied to process the time series data of vocal melodies, addressing inconsistent feature dimensions. The second-order difference method extracts tonal trend features. We introduce a fuzzy C-means clustering method enhanced by quantum particle swarm optimization (QPSO) to manage data uncertainties, improving classification accuracy and convergence speed. Additionally, we employ a cross-correlation function to eliminate uncertainties from tonal transition redundancies. We designed a detection algorithm using trend data to validate our clustering method, thereby enhancing the accuracy of the analysis of tonal ranges and potential models. This method detects whether Yue Opera adheres to traditional rhythmic norms and models the regularity of musical tones and vocal patterns. Simulation results reveal that our approach achieves a 91.4% accuracy in classifying vocal styles, surpassing traditional methods and demonstrating its potential for identifying various styles. This research offers technical support for Yue Opera music education and interdisciplinary research. The findings enhance the quality of artistic creation and performance in Yue Opera, ensuring its preservation and development.</p></div>2025-01-24T18:57:49ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0313065.s006https://figshare.com/articles/dataset/S6_Data_-/28275716CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/282757162025-01-24T18:57:49Z |
| spellingShingle | S6 Data - Yuhang Zhang (3144870) Science Policy Plant Biology Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified traditional rhythmic norms time series data surpassing traditional methods simulation results reveal identifying various styles tonal transition redundancies manage data uncertainties classifying vocal styles means </ p improving classification accuracy clustering tonal trends chinese yue opera yue opera tonal ranges eliminate uncertainties vocal patterns vocal melodies clustering method xlink "> thereby enhancing study develops musical tones linear interpolation interdisciplinary research innovative method fuzzy c findings enhance correlation function convergence speed artistic creation approach achieves |
| status_str | publishedVersion |
| title | S6 Data - |
| title_full | S6 Data - |
| title_fullStr | S6 Data - |
| title_full_unstemmed | S6 Data - |
| title_short | S6 Data - |
| title_sort | S6 Data - |
| topic | Science Policy Plant Biology Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified traditional rhythmic norms time series data surpassing traditional methods simulation results reveal identifying various styles tonal transition redundancies manage data uncertainties classifying vocal styles means </ p improving classification accuracy clustering tonal trends chinese yue opera yue opera tonal ranges eliminate uncertainties vocal patterns vocal melodies clustering method xlink "> thereby enhancing study develops musical tones linear interpolation interdisciplinary research innovative method fuzzy c findings enhance correlation function convergence speed artistic creation approach achieves |