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ccmusic-database/
erhu_playing_tech

Name: erhu_playing_tech
Availability: InStock

music

art

audio-classification

ccmusic-database/erhu_playing_tech

mit

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Intro

The Erhu Performance Technique Recognition Model is an audio analysis tool based on deep learning techniques, aiming to automatically distinguish different techniques in erhu performance. By deeply analyzing the acoustic characteristics of erhu music, the model is able to recognize 11 basic playing techniques, including split bow, pad bow, overtone, continuous bow, glissando, big glissando, strike bow, pizzicato, throw bow, staccato bow, vibrato, tremolo and vibrato. Through time-frequency conversion, feature extraction and pattern recognition, the model can accurately categorize the complex techniques of erhu performance, which provides an efficient technical support for music information retrieval, music education, and research on the art of erhu performance. The application of this model not only enriches the research in the field of music acoustics, but also opens up a new way for the inheritance and innovation of traditional music.

Demo (inference code)

https://huggingface.co/spaces/ccmusic-database/erhu-playing-tech

Usage

python

from huggingface_hub import snapshot_download

model_dir = snapshot_download(
    "ccmusic-database/erhu_playing_tech",
    cache_dir="./__pycache__",
)
print(model_dir)

Maintenance

bash

GIT_LFS_SKIP_SMUDGE=1 git clone git@hf.co:ccmusic-database/erhu_playing_tech
cd erhu_playing_tech

Results

Backbone	Mel	CQT	Chroma
Swin-S	0.978	0.940	0.903
Swin-T	0.994	0.958	0.957

AlexNet	0.960	0.970	0.933
ConvNeXt-T	0.994	0.993	0.954
ShuffleNet-V2-X2.0	0.990	0.923	0.887
GoogleNet	0.986	0.981	0.908
SqueezeNet1.1	0.932	0.939	0.875
Average	0.976	0.958	0.917

Best Result

A demo result of Swin-T fine-tuning by mel:

Loss curve
Training and validation accuracy
Confusion matrix

Dataset

https://huggingface.co/datasets/ccmusic-database/erhu_playing_tech

Mirror

https://www.modelscope.cn/models/ccmusic-database/erhu_playing_tech

Evaluation

https://github.com/monetjoe/ccmusic_eval

Cite

bibtex

@article{Zhou-2025,
  author  = {Monan Zhou and Shenyang Xu and Zhaorui Liu and Zhaowen Wang and Feng Yu and Wei Li and Baoqiang Han},
  title   = {CCMusic: An Open and Diverse Database for Chinese Music Information Retrieval Research},
  journal = {Transactions of the International Society for Music Information Retrieval},
  volume  = {8},
  number  = {1},
  pages   = {22--38},
  month   = {Mar},
  year    = {2025},
  url     = {https://doi.org/10.5334/tismir.194},
  doi     = {10.5334/tismir.194}
}