README.md

---
license: cc-by-nc-nd-4.0
task_categories:
- audio-classification
language:
- zh
- en
tags:
- music
- art
pretty_name: Guzheng Technique 99 Dataset
size_categories:
- n<1K
viewer: false
---

# Dataset Card for Guzheng Technique 99 Dataset
The original dataset, sourced from [Guzheng_Tech99](https://ccmusic-database.github.io/en/database/csmtd.html#Tech99), encompasses 99 solo compositions for the guzheng, recorded by professional musicians in a studio environment, with a cumulative duration of 9,064.6 seconds. Each composition has been annotated for every note, indicating the onset, offset, pitch, and playing techniques, which include chanyin, boxian, shanghua, xiahua, huazhi\guazou\lianmo\liantuo, yaozhi, and dianyin. This meticulous annotation has resulted in a total of 63,352 annotated labels across the dataset.

Based on the above original data, we performed data processing and constructed the [default subset](#default-subset-1) of the current integrated version of the dataset, and the details of its data structure can be viewed through the [viewer](https://www.modelscope.cn/datasets/ccmusic-database/Guzheng_Tech99/dataPeview). In light of the fact that the current dataset has been referenced and evaluated in a published article, we transcribe here the details of the dataset processing during the evaluation in the said article: each audio clip is a 3-second segment sampled at 44,100Hz, which is then converted into a log Constant-Q Transform (CQT) spectrogram. A CQT accompanied by a label constitutes a single data entry, forming the first and second columns, respectively. The CQT is a 3-dimensional array with dimensions of 88×258×1, representing the frequency-time structure of the audio. The label, on the other hand, is a 2-dimensional array with dimensions of 7×258, indicating the presence of seven distinct techniques across each time frame. Ultimately, given that the original dataset has already been divided into train, valid, and test sets, we have integrated the feature extraction method mentioned in this article's evaluation process into the API, thereby constructing the [eval subset](#eval-subset-1), which is not embodied in our paper.

## Viewer
<https://www.modelscope.cn/datasets/ccmusic-database/Guzheng_Tech99/dataPeview>

## Dataset Structure
### Default Subset
<style>
  .datastructure td {
    vertical-align: middle !important;
    text-align: center;
  }
  .datastructure th {
    text-align: center;
  }
</style>
<table class="datastructure">
    <tr>
        <th>audio</th>
        <th>mel</th>
        <th>label</th>
    </tr>
    <tr>
        <td>.flac, 44100Hz</td>
        <td>.jpg, 44100Hz</td>
        <td>{onset_time : float64, offset_time : float, IPT : 7-class, note : int8}</td>
    </tr>
    <tr>
        <td>...</td>
        <td>...</td>
        <td>...</td>
    </tr>
</table>

### Eval Subset
| data(logCQT spectrogram) |      label       |
| :----------------------: | :--------------: |
|  float64, 88 x 258 x 1   | float64, 7 x 258 |
|           ...            |       ...        |

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

### Data Fields
The dataset comprises 99 Guzheng solo compositions, recorded by professionals in a studio, totaling 9064.6 seconds. It includes seven playing techniques labeled for each note (onset, offset, pitch, vibrato, point note, upward portamento, downward portamento, plucks, glissando, and tremolo), resulting in 63,352 annotated labels. The dataset is divided into 79, 10, and 10 songs for the training, validation, and test sets, respectively.

### Data Splits
train, validation, 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/Guzheng_Tech99>
- **Point of Contact:** <https://github.com/LiDCC/GuzhengTech99/tree/windows>

### Dataset Summary
The integrated version provides the original content and the spectrogram generated in the experimental part of the paper cited above. For the second part, the pre-process in the paper is replicated. Each audio clip is a 3-second segment sampled at 44,100Hz, which is subsequently converted into a log Constant-Q Transform (CQT) spectrogram. A CQT accompanied by a label constitutes a single data entry, forming the first and second columns, respectively. The CQT is a 3-dimensional array with the dimension of 88 × 258 × 1, representing the frequency-time structure of the audio. The label, on the other hand, is a 2-dimensional array with dimensions of 7 × 258, which indicates the presence of seven distinct techniques across each time frame. indicating the existence of the seven techniques in each time frame. In the end, given that the raw dataset has already been split into train, valid, and test sets, the integrated dataset maintains the same split method. This dataset can be used for frame-level guzheng playing technique detection.

### Supported Tasks and Leaderboards
MIR, audio classification

### Languages
Chinese, English

## Usage
### Default Subset
```python
from datasets import load_dataset

ds = load_dataset("ccmusic-database/Guzheng_Tech99", name="default", split="train")
for item in ds:
    print(item)
```

### Eval Subset
```python
from datasets import load_dataset

ds = load_dataset("ccmusic-database/Guzheng_Tech99", name="eval")
for item in ds["train"]:
    print(item)

for item in ds["validation"]:
    print(item)

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

## Maintenance
```bash
git clone [email protected]:datasets/ccmusic-database/Guzheng_Tech99
cd Guzheng_Tech99
```
## Dataset Creation
### Curation Rationale
Instrument playing technique (IPT) is a key element of musical presentation.

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

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

### Annotations
#### Annotation process
Guzheng is a polyphonic instrument. In Guzheng performance, notes with different IPTs are usually overlapped and mixed IPTs that can be decomposed into multiple independent IPTs are usually used. Most existing work on IPT detection typically uses datasets with monophonic instrumental solo pieces. This dataset fills a gap in the research field.

#### 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
[Dichucheng Li, Mingjin Che, Wenwu Meng, Yulun Wu, Yi Yu, Fan Xia and Wei Li. "Frame-Level Multi-Label Playing Technique Detection Using Multi-Scale Network and Self-Attention Mechanism", in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023).](https://arxiv.org/pdf/2303.13272.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