Clustering analysis of Yue opera character tone trends based on quantum particle swarm optimization for fuzzy C-means.

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 an

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC11760033 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature