README.md

---
license: cc-by-nc-nd-4.0
task_categories:
- audio-classification
language:
- zh
- en
tags:
- music
- art
pretty_name: GZ_IsoTech Dataset
size_categories:
- n<1K
viewer: false
---

# Dataset Card for GZ_IsoTech Dataset
The raw dataset, sourced from [GZ_IsoTech](https://ccmusic-database.github.io/en/database/csmtd.html#GZTech), comprises 2,824 audio clips showcasing various guzheng playing techniques. Specifically, 2,328 clips were sourced from virtual sound banks, while 496 clips were performed by a skilled professional guzheng artist. These recordings encompass a comprehensive range of tones inherent to the guzheng instrument. Categorization of the clips is based on the diverse playing techniques characteristic of the guzheng; the clips are divided into eight categories: Vibrato (chanyin), Upward Portamento (shanghuayin), Downward Portamento (xiahuayin), Returning Portamento (huihuayin), Glissando (guazou, huazhi), Tremolo (yaozhi), Harmonic (fanyin), Plucks (gou, da, mo, tuo…).

Based on the aforementioned raw dataset, we conducted data processing to construct the `default subset` of the current integrated version of the dataset. Due to the pre-existing split in the raw dataset, wherein the data has been partitioned approximately in a 4:1 ratio for training and testing sets, we uphold the original data division approach for the default subset. The data structure of the default subset can be viewed in the [viewer](https://www.modelscope.cn/datasets/ccmusic-database/GZ_IsoTech/dataPeview). The `eval subset` was not further constructed as the original dataset had already been cited and used in published articles.

## Viewer
<https://www.modelscope.cn/datasets/ccmusic-database/GZ_IsoTech/dataPeview>

## Dataset Structure
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  .datastructure td {
    vertical-align: middle !important;
    text-align: center;
  }
  .datastructure th {
    text-align: center;
  }
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<table class="datastructure">
    <tr>
        <th>audio</th>
        <th>mel</th>
        <th>label</th>
        <th>cname</th>
    </tr>
    <tr>
        <td>.wav, 44100Hz</td>
        <td>.jpg, 44100Hz</td>
        <td>8-class</td>
        <td>string</td>
    </tr>
    <tr>
        <td>...</td>
        <td>...</td>
        <td>...</td>
        <td>...</td>
    </tr>
</table>

### Data Instances
.zip(.flac, .csv)

### Data Fields
Categorization of the clips is based on the diverse playing techniques characteristic of the guzheng, the clips are divided into eight categories: Vibrato (chanyin), Upward Portamento (shanghuayin), Downward Portamento (xiahuayin), Returning Portamento (huihuayin), Glissando (guazou, huazhi), Tremolo (yaozhi), Harmonic (fanyin), Plucks (gou, da, mo, tuo…).
<img src="https://www.modelscope.cn/api/v1/datasets/ccmusic-database/GZ_IsoTech/repo?Revision=master&FilePath=.%2Fdata%2Fiso.png&View=true">

### Data Splits
train, test

## Dataset Description
- **Homepage:** <https://ccmusic-database.github.io>
- **Repository:** <https://huggingface.co/datasets/ccmusic-database/Guzheng_Tech99>
- **Paper:** <https://doi.org/10.5281/zenodo.5676893>
- **Leaderboard:** <https://www.modelscope.cn/datasets/ccmusic-database/GZ_IsoTech>
- **Point of Contact:** <https://arxiv.org/abs/2209.08774>

### Dataset Summary
Due to the pre-existing split in the raw dataset, wherein the data has been partitioned approximately in a 4:1 ratio for training and testing sets, we uphold the original data division approach. In contrast to utilizing platform-specific automated splitting mechanisms, we directly employ the pre-split data for subsequent integration steps.

### Supported Tasks and Leaderboards
MIR, audio classification

### Languages
Chinese, English

## Usage
```python
from datasets import load_dataset

dataset = load_dataset("ccmusic-database/GZ_IsoTech")
for item in ds["train"]:
    print(item)

for item in ds["test"]:
    print(item)
```

## Maintenance
```bash
GIT_LFS_SKIP_SMUDGE=1 git clone git@hf.co:datasets/ccmusic-database/GZ_IsoTech
cd GZ_IsoTech
```

## Dataset Creation
### Curation Rationale
The Guzheng is a kind of traditional Chinese instrument with diverse playing techniques. Instrument playing techniques (IPT) play an important role in musical performance. However, most of the existing works for IPT detection show low efficiency for variable-length audio and do not assure generalization as they rely on a single sound bank for training and testing. In this study, we propose an end-to-end Guzheng playing technique detection system using Fully Convolutional Networks that can be applied to variable-length audio. Because each Guzheng playing technique is applied to a note, a dedicated onset detector is trained to divide an audio into several notes and its predictions are fused with frame-wise IPT predictions. During fusion, we add the IPT predictions frame by frame inside each note and get the IPT with the highest probability within each note as the final output of that note. We create a new dataset named GZ_IsoTech from multiple sound banks and real-world recordings for Guzheng performance analysis. Our approach achieves 87.97% in frame-level accuracy and 80.76% in note-level F1 score, outperforming existing works by a large margin, which indicates the effectiveness of our proposed method in IPT detection.

### Source Data
#### Initial Data Collection and Normalization
Dichucheng Li, Monan Zhou

#### Who are the source language producers?
Students from FD-LAMT

### Annotations
#### Annotation process
This database contains 2824 audio clips of guzheng playing techniques. Among them, 2328 pieces were collected from virtual sound banks, and 496 pieces were played and recorded by a professional guzheng performer.

#### Who are the annotators?
Students from FD-LAMT

### Personal and Sensitive Information
None

## Considerations for Using the Data
### Social Impact of Dataset
Promoting the development of the music AI industry

### Discussion of Biases
Only for Traditional Chinese Instruments

### Other Known Limitations
Insufficient sample 

## Additional Information
### Dataset Curators
Dichucheng Li

### Evaluation
[Li, Dichucheng, Yulun Wu, Qinyu Li, Jiahao Zhao, Yi Yu, Fan Xia and Wei Li. “Playing Technique Detection by Fusing Note Onset Information in Guzheng Performance.” International Society for Music Information Retrieval Conference (2022).](https://archives.ismir.net/ismir2022/paper/000037.pdf)

### Citation Information
```bibtex
@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 and General Music Information Retrieval Research},
  month        = {mar},
  year         = {2024},
  publisher    = {HuggingFace},
  version      = {1.2},
  url          = {https://huggingface.co/ccmusic-database}
}
```

### Contributions
Promoting the development of the music AI industry