Skip to content
DraconiticResearch
Search Sources API
201,818 studies31 sourcesUpdated 23 min ago
PreprintCC BY 4.0arXiv

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

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.

Related

  1. Optimising instrument concepts with Machine Learning: Application to CMB spectral distortion experiments
    Xavier Coulon, Nabila Aghanim, Bruno Maffei
    HALPreprintCC BY 4.0Full textCosmologyInstrumentation & methodsHALarXiv
  2. Plasmonic Lithography Optical Proximity Correction Method Based on Decomposed Machine Learning Fast Imaging Model
    zhenyu xing, Huwen Ding, pengyu ren +1
    Optics ExpressPeer-reviewedLicence not statedOptics & photonicsCrossref
  3. Compact and stable representation of real-frequency spectral functions for machine learning
    Xinyang Dong, Ido Zemach, Lei Zhang +2
    Physical Review ResearchPeer-reviewedCC BY 4.0Atomic, molecular & optical physicsCrossref
  4. Failure classification of wind turbine operational conditions using hybrid machine learning
    Marcela R Machado, Amanda Aryda S Sousa, Jefferson S Coelho +1
    Wind Energy SciencePeer-reviewedPublisher termsFull textGeneral physicsHAL
  5. Exoskeleton-based user's pose estimation: a hybrid geometric and machine learning approach
    Maël Gallois, Sylvain Guegan, Nicolas Vignais +2
    Multidisciplinary Biomechanics JournalPeer-reviewedCC BY 4.0Full textGeneral physicsHAL
BibTeX
@misc{xia2026timbre,
  title = {{Timbre Analysis of the Hulusi, a Southwestern Chinese Free-Reed Instrument, using Machine Learning}},
  author = {Yang Xia and Rolf Bader},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.17612},
  eprint = {2609.17612},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.17612},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - Timbre Analysis of the Hulusi, a Southwestern Chinese Free-Reed Instrument, using Machine Learning
AU  - Yang Xia
AU  - Rolf Bader
PY  - 2026
DA  - 2026/09/14
PB  - arXiv
DO  - 10.48550/arxiv.2609.17612
UR  - https://arxiv.org/abs/2609.17612
AB  - 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.
ER  -