Intro 简介

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.

古筝演奏技法识别模型是基于古筝演奏技法数据集训练的，该数据集包含2,824个音频片段，展示了各种古筝演奏技巧的特征。数据集中的2,328个音频片段来自虚拟声音库，496个片段由一位技艺高超的专业古筝艺术家演奏，涵盖了古筝乐器固有的全面音调范围。这些音频片段根据古筝特有的演奏技巧被划分为八个类别：颤音（chanyin）、上滑音（shanghuayin）、下滑音（xiahuayin）、回滑音（huihuayin）、刮奏（guazou, huazhi等）、摇指（yaozhi）、泛音（fanyin）以及拨弦技巧（gou, da, mo, tuo等）。该模型通过对这些音频片段进行特征提取、时域与频域分析、以及模式识别，能够准确识别出不同古筝演奏技巧。该模型的应用能够为古筝演奏技巧的自动识别、数字化分析与教学研究提供有力支持，推动古筝艺术的传承与创新。

Demo 在线演示

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

Usage 使用

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

Maintenance 维护

git clone [email protected]:ccmusic-database/GZ_IsoTech
cd GZ_IsoTech

Results 训练结果

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 最佳结果

Loss curve
Training and validation accuracy
Confusion matrix

Dataset 数据集

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

Mirror 镜像

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

Evaluation 校验

https://github.com/monetjoe/ccmusic_eval

Cite 引用

@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