Intro

The Guzheng Performance Technique Recognition Model is trained on the GZ_IsoTech Dataset, which consists of 2,824 audio clips that showcase various Guzheng playing techniques. Of these, 2,328 clips are from a virtual sound library, and 496 clips are performed by a highly skilled professional Guzheng artist, covering the full tonal range inherent to the Guzheng instrument. The audio clips are categorized into eight different playing techniques based on the unique performance practices of the Guzheng: Vibrato (chanyin), Slide-up (shanghuayin), Slide-down (xiahuayin), Return Slide (huihuayin), Glissando (guazou, huazhi, etc.), Thumb Plucking (yaozhi), Harmonics (fanyin), and Plucking Techniques (gou, da, mo, tuo, etc.). The model utilizes feature extraction, time-domain and frequency-domain analysis, and pattern recognition to accurately identify these distinct Guzheng playing techniques. The application of this model provides strong support for the automatic recognition, digital analysis, and educational research of Guzheng performance techniques, promoting the preservation and innovation of Guzheng art.

Demo

https://huggingface.co/spaces/ccmusic-database/GZ_IsoTech

Usage

from modelscope import snapshot_download
model_dir = snapshot_download("ccmusic-database/GZ_IsoTech")

Maintenance

git clone git@hf.co:ccmusic-database/GZ_IsoTech
cd GZ_IsoTech

Results

Backbone	Size(M)	Mel	CQT	Chroma
vit_l_16	304.3	*0.855*	*0.824*	*0.770*
maxvit_t	30.9	0.763	0.776	0.642

resnext101_64x4d	83.5	0.713	0.765	0.639
resnet101	44.5	0.731	0.798	*0.719*
regnet_y_8gf	39.4	0.804	*0.807*	0.716
shufflenet_v2_x2_0	7.4	0.702	0.799	0.665
mobilenet_v3_large	5.5	*0.806*	0.798	0.657

Best result

Loss curve
Training and validation accuracy
Confusion matrix

Dataset

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

Mirror

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

Evaluation

https://github.com/monetjoe/ccmusic_eval

Cite

@dataset{zhaorui_liu_2021_5676893,
  author       = {Monan Zhou, Shenyang Xu, Zhaorui Liu, Zhaowen Wang, Feng Yu, Wei Li and Baoqiang Han},
  title        = {CCMusic: an Open and Diverse Database for Chinese Music Information Retrieval Research},
  month        = {mar},
  year         = {2024},
  publisher    = {HuggingFace},
  version      = {1.2},
  url          = {https://huggingface.co/ccmusic-database}
}

ccmusic-database
/

GZ_IsoTech