Clustering analysis of Yue opera character tone trends based on quantum particle swarm optimization for fuzzy C-means
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 diffe...
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| creator | Zhang, Yuhang Wu, Xiaofeng Xu, Jiawei Ning, Zihao Han, Xiao |
| description | 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. |
| doi_str_mv | 10.1371/journal.pone.0313065 |
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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.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0313065</identifier><identifier>PMID: 39854432</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Accuracy ; Algorithms ; Analysis ; Biology and Life Sciences ; Chinese operas ; Classification ; Cluster Analysis ; Clustering ; Correlation ; Cross correlation ; Data analysis ; Datasets ; Digitization ; Fuzzy Logic ; Humans ; Interdisciplinary research ; Interdisciplinary studies ; Interpolation ; Mathematical optimization ; Melody ; Methods ; Music ; Music in education ; Noise ; Optimization ; Particle swarm optimization ; Physical Sciences ; Research and Analysis Methods ; Singing ; Social Sciences ; Trends ; Uncertainty</subject><ispartof>PloS one, 2025-01, Vol.20 (1), p.e0313065</ispartof><rights>Copyright: © 2025 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</rights><rights>COPYRIGHT 2025 Public Library of Science</rights><rights>2025 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2025 Zhang et al 2025 Zhang et al</rights><rights>2025 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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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. 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The findings enhance the quality of artistic creation and performance in Yue Opera, ensuring its preservation and development.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Analysis</subject><subject>Biology and Life Sciences</subject><subject>Chinese operas</subject><subject>Classification</subject><subject>Cluster Analysis</subject><subject>Clustering</subject><subject>Correlation</subject><subject>Cross correlation</subject><subject>Data analysis</subject><subject>Datasets</subject><subject>Digitization</subject><subject>Fuzzy Logic</subject><subject>Humans</subject><subject>Interdisciplinary research</subject><subject>Interdisciplinary studies</subject><subject>Interpolation</subject><subject>Mathematical optimization</subject><subject>Melody</subject><subject>Methods</subject><subject>Music</subject><subject>Music in education</subject><subject>Noise</subject><subject>Optimization</subject><subject>Particle swarm 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One</addtitle><date>2025-01-24</date><risdate>2025</risdate><volume>20</volume><issue>1</issue><spage>e0313065</spage><pages>e0313065-</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>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.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>39854432</pmid><doi>10.1371/journal.pone.0313065</doi><tpages>e0313065</tpages><orcidid>https://orcid.org/0009-0004-6683-3416</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Accuracy Algorithms Analysis Biology and Life Sciences Chinese operas Classification Cluster Analysis Clustering Correlation Cross correlation Data analysis Datasets Digitization Fuzzy Logic Humans Interdisciplinary research Interdisciplinary studies Interpolation Mathematical optimization Melody Methods Music Music in education Noise Optimization Particle swarm optimization Physical Sciences Research and Analysis Methods Singing Social Sciences Trends Uncertainty |
| title | Clustering analysis of Yue opera character tone trends based on quantum particle swarm optimization for fuzzy C-means |
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