Research on character tone trend clustering of Kunqu Opera based on quantum adaptive genetic algorithm

Abstract

Abstract Kunqu, one of the oldest forms of Chinese opera, features a unique artistic expression arising from the interplay between vocal melody and the tonal quality of its lyrics. Identifying Kunqu’s character tone trend (vocal melodies derived from tonal quality of the lyrics) is critical to understanding and preserving this art form. Traditional research methods, which rely on qualitative descriptions by musicologists, have often been debated due to their subjective nature. In this study, we present a novel approach to analyze the character tone trend in Kunqu by employing computer modeling machine learning techniques. By extracting the character tone trend of Kunqu using computational modeling methods and employing machine learning techniques to apply cluster analysis on Kunqu’s character tone melody, our model uncovers musical structural patterns between singing and speech, validating and refining the qualitative findings of musicologists. Furthermore, our model can automatically assess whether a piece adheres to the rhythmic norms of ‘the integration of literature and music’ in Kunqu, thus contributing to the digitization, creation, and preservation of this important cultural heritage.