README.md

---
license: cc-by-nc-nd-4.0
task_categories:
- audio-classification
- image-classification
language:
- zh
- en
tags:
- music
- art
pretty_name: Bel Conto and Chinese Folk Song Singing Tech
size_categories:
- 1K<n<10K
viewer: false
---

# Dataset Card for Bel Conto and Chinese Folk Song Singing Tech
The original dataset, sourced from the [Bel Canto and National Singing Dataset](https://ccmusic-database.github.io/en/database/ccm.html#shou9), contains 203 acapella singing clips performed in two styles, Bel Canto and Chinese folk singing style, by professional vocalists. All of them are sung by professional vocalists and were recorded in professional commercial recording studios.

Based on the aforementioned original dataset, we have constructed the [default subset](#default-subset-1) of the current integrated version of the dataset, and its data structure can be viewed in the [viewer](https://www.modelscope.cn/datasets/ccmusic-database/bel_canto/dataPeview). Since the default subset has not been evaluated, to verify its effectiveness, we have built the [eval subset](#eval-subset-1) based on the default subset for the evaluation of the integrated version of the dataset. The evaluation results can be seen in the [bel_canto](https://www.modelscope.cn/models/ccmusic-database/bel_canto). Below are the data structures and invocation methods of the subsets.

## Dataset Structure
<style>
  .belcanto td {
    vertical-align: middle !important;
    text-align: center;
  }
  .belcanto th {
    text-align: center;
  }
</style>

### Default Subset
<table class="belcanto">
    <tr>
        <th>audio</th>
        <th>mel (spectrogram)</th>
        <th>label (4-class)</th>
        <th>gender (2-class)</th>
        <th>singing_method(2-class)</th>
    </tr>
    <tr>
        <td>.wav, 22050Hz</audio></td>
        <td>.jpg, 22050Hz</td>
        <td>m_bel, f_bel, m_folk, f_folk</td>
        <td>male, female</td>
        <td>Folk_Singing, Bel_Canto</td>
    </tr>
    <tr>
        <td>...</td>
        <td>...</td>
        <td>...</td>
        <td>...</td>
        <td>...</td>
    </tr>
</table>

### Eval Subset
<table class="belcanto">
    <tr>
        <th>mel</th>
        <th>cqt</th>
        <th>chroma</th>
        <th>label (4-class)</th>
        <th>gender (2-class)</th>
        <th>singing_method (2-class)</th>
    </tr>
    <tr>
        <td>.jpg, 1.6s, 22050Hz</td>
        <td>.jpg, 1.6s, 22050Hz</td>
        <td>.jpg, 1.6s, 22050Hz</td>
        <td>m_bel, f_bel, m_folk, f_folk</td>
        <td>male, female</td>
        <td>Folk_Singing, Bel_Canto</td>
    </tr>
    <tr>
        <td>...</td>
        <td>...</td>
        <td>...</td>
        <td>...</td>
        <td>...</td>
        <td>...</td>
    </tr>
</table>

### Data Instances
.zip(.wav, .jpg)

### Data Fields
m_bel, f_bel, m_folk, f_folk

### Data Splits 
| Split(8:1:1) / Subset |       default       |        eval         |
| :-------------------: | :-----------------: | :-----------------: |
|         train         |         159         |        7907         |
|      validation       |         21          |         988         |
|         test          |         23          |         991         |
|         total         |         203         |        9886         |
|   total duration(s)   | `18192.37652721089` | `18192.37652721089` |

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

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

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

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

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

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

ds = load_dataset("ccmusic-database/bel_canto", 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/bel_canto
cd bel_canto
```

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

### Dataset Summary
This database contains hundreds of acapella singing clips that are sung in two styles, Bel Conto and Chinese national singing style by professional vocalists. All of them are sung by professional vocalists and were recorded in professional commercial recording studios.

|     Statistical items 统计项     |      Values 值       |
| :------------------------------: | :------------------: |
|       Total count 总数据量       |        `203`         |
|   Total duration(s) 总时长(秒)   | `18270.477865079374` |
|  Mean duration(s) 平均时长(秒)   | `90.00235401516929`  |
|   Min duration(s) 最短时长(秒)   |        `13.7`        |
|   Max duration(s) 最长时长(秒)   |       `310.0`        |
| Class with max durs 最长时长类别 |  `Bel Canto Female`  |

#### Totals 总量统计
|      Subset       |       default        |         eval         |
| :---------------: | :------------------: | :------------------: |
|       Total       |         203          |         9910         |
| Total duration(s) | `18270.477865079367` | `18270.477865079367` |

### Supported Tasks and Leaderboards
Audio classification, Image classification, singing method classification, voice classification

### Languages
Chinese, English

## Dataset Creation
### Curation Rationale
Lack of a dataset for Bel Conto and Chinese folk song singing tech

### Source Data
#### Initial Data Collection and Normalization
Zhaorui Liu, Monan Zhou

#### Who are the source language producers?
Students from CCMUSIC

### Annotations
#### Annotation process
All of them are sung by professional vocalists and were recorded in professional commercial recording studios.

#### Who are the annotators?
professional vocalists

### Personal and Sensitive Information
None

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

### Discussion of Biases
Only for Chinese songs

### Other Known Limitations
Some singers may not have enough professional training in classical or ethnic vocal techniques.

## Additional Information
### Dataset Curators
Zijin Li

### Evaluation
<https://huggingface.co/ccmusic-database/bel_canto>

### 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
Provide a dataset for distinguishing Bel Conto and Chinese folk song singing tech