---
title: GZ_IsoTech
canonical_url: "https://www.modelscope.cn/models/ccmusic-database/GZ_IsoTech"
md_url: "https://www.modelscope.cn/models/ccmusic-database/GZ_IsoTech.md"
repository: ccmusic-database/GZ_IsoTech
chinese_name: "古筝演奏技法识别模型"
last_updated: 2026-08-05
license: "MIT License"
downloads: 1130
stars: 14
---

# GZ_IsoTech

> GZ_IsoTech - ccmusic-database 在 ModelScope 开源的模型。古筝演奏技法识别模型是一种先进的技术系统，专门用于自动识别和分类古筝演奏中的各种技法动作。通过深度学习算法和大量标注数据的训练，该模型能够精准分析演奏视频或音频，快速识别出如勾、抹、托、颤音、滑音等复杂技法，并将其归类到相应的类别中。它不仅为古筝教学提供了高效、客观的评估工具，帮助学习者快速掌握技法要点，还为音乐学者和演奏家提供了深入研究演奏风格和技术演变的有力支持，极大地促进了古筝艺术的数字化传承与创新发展。

ccmusic-database/GZ_IsoTech 是 ModelScope 魔搭社区上的机器学习模型，采用 MIT License 许可。

- **Repository**: ccmusic-database/GZ_IsoTech
- **License**: MIT License
- **Downloads**: 1130
- **Stars**: 14
- **Last updated**: 2026-08-05

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

---

# 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 (推理代码)
<https://www.modelscope.cn/studios/ccmusic-database/GZ_IsoTech>

## Usage 使用
:modelscope-code[]{type="sdk"}

## Maintenance 维护
```bash
GIT_LFS_SKIP_SMUDGE=1 
```

:modelscope-code[]{type="git"}

## Results 训练结果
|      Backbone      | Size(M) |                 Mel                  |     CQT     |   Chroma    |
| :----------------: | :-----: | :----------------------------------: | :---------: | :---------: |
|      vit_l_16      |  304.3  | [**_0.855_**](#best-result-最佳结果) | **_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
<table>
    <tr>
        <th>Loss curve</th>
        <td><img src="./vit_l_16_mel_2024-12-06_08-28-13/loss.jpg"></td>
    </tr>
    <tr>
        <th>Training and validation accuracy</th>
        <td><img src="./vit_l_16_mel_2024-12-06_08-28-13/acc.jpg"></td>
    </tr>
    <tr>
        <th>Confusion matrix</th>
        <td><img src="./vit_l_16_mel_2024-12-06_08-28-13/mat.jpg"></td>
    </tr>
</table>

## Dataset 数据集
<https://www.modelscope.cn/datasets/ccmusic-database/GZ_IsoTech>

## Mirror 镜像
<https://huggingface.co/ccmusic-database/GZ_IsoTech>

## Evaluation 校验
<https://github.com/monetjoe/ccmusic_eval>

## Cite 引用
```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}
}
```