Timbre Analysis of the Hulusi, a Southwestern Chinese Free-Reed Instrument, using Machine Learning

2026-09-14

AI Summary

研究使用机器学习模型分析葫芦丝的音色特征，通过七个心理声学特征对不同乐器和音高进行聚类。

Abstract

The hulusi is a wind instrument that was invented in Yunnan Province, China, and has become tremendously popular in recent years. It consists of a mouthpiece, a gourd, and three bamboo tubes, all with free reeds made of copper. The main bamboo tube in the middle has seven finger holes. In this instrument, the pipe length, not the free reed's eigenfrequency, determines the instrument's pitch, unlike, for example, with the Western accordion or the blues harp. In this study, a machine learning model implemented in the COMSAR framework (https://github.com/ifsm) was used to investigate the timbre characteristics of the \emph{hulusi} to cluster different instruments and pitches. The measured \emph{hulusi} pitches C, B, A, G, and F were analyzed according to seven psychoacoustic features, among which only the spectral centroid, sharpness, and fractal correlation dimension are shown to form pitch clusters. These timbre features were used to train Kohonen self-organizing maps (SOMs) for clustering. Brightness and sharpness analysis revealed that the highest pitches were less bright and less sharp than mid- and low-range pitches were. Furthermore, the fractal correlation dimension, which mainly determines the chaoticity of the initial transients, was the best-clustering timbre feature for the hulusi, with the highest pitches showing the least chaoticity. This result is supported by defining a cluster quality index for the SOMs.

Problem

• timbre analysis
• hulusi
• pitch clustering

Innovation

• machine learning
• timbre analysis
• self-organizing maps (SOMs)
• hulusi
• fractal correlation dimension