Analysis of Guqin Playing Techniques Based on Artificial Intelligence

Abstract

The Guqin art form is one of China’s oldest musical traditions and is recognized as a significant part of the world’s intangible cultural heritage. Numerous ancient scores have survived to the present day, but only a select few have been adapted for contemporary music. While ancient Guqin scores can be translated into modern numbered or staff notation, their rhythmic elements cannot be accurately replicated. The process of converting these scores into a simplified Guqin notation presents challenges, particularly in selecting appropriate fingerings. To address the issues related to playing ancient scores and transferring new ones, this paper introduces a triple representation method for Guqin score knowledge using knowledge mapping technology. It transforms the simplified Guqin notation into a text format, exemplified by the Ming Dynasty’s “Magic Secret Score.” A computer-editable text corpus of Guqin is created, and various tags are applied to the text. A word cloud visualization of the “Magic Secret Score” text spectrum is generated using a word cloud tool. The frequency of different right-hand fingerings in the “Magic Secret Score” is analyzed. By examining the temporal characteristics of the music, the paper extracts the timing relationships of right-hand fingerings from the text corpus and identifies various performance templates using the KMP pattern matching algorithm. Specifically, it analyzes 64 different right-hand finger techniques. Additionally, the frequency of string combinations in the “Magic Secret Score” is recorded, providing essential guidelines for future intelligent music transfer reasoning. The experimental findings indicate that there are specific constraints on the timing of fingerings and string usage in musical tones, with the maximum length of reusable fingering timing templates being no more than 25.