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
dataset_info:
  - config_name: default
    features:
      - name: audio
        dtype:
          audio:
            sampling_rate: 22050
      - name: mel
        dtype: image
      - name: label
        dtype: int8
      - name: gender
        dtype: int8
      - name: singing_method
        dtype: int8
    splits:
      - name: train
        num_bytes: 74400
        num_examples: 203
    download_size: 1221158882
    dataset_size: 74400
  - config_name: eval
    features:
      - name: mel
        dtype: image
      - name: cqt
        dtype: image
      - name: chroma
        dtype: image
      - name: label
        dtype: int8
      - name: gender
        dtype: int8
      - name: singing_method
        dtype: int8
    splits:
      - name: train
        num_bytes: 4463751
        num_examples: 7926
      - name: validation
        num_bytes: 557511
        num_examples: 990
      - name: test
        num_bytes: 559833
        num_examples: 994
    download_size: 889320608
    dataset_size: 5581095
configs:
  - config_name: default
    data_files:
      - split: train
        path: default/train/data-*.arrow
  - config_name: eval
    data_files:
      - split: train
        path: eval/train/data-*.arrow
      - split: validation
        path: eval/validation/data-*.arrow
      - split: test
        path: eval/test/data-*.arrow
---

# Dataset Card for Bel Conto and Chinese Folk Song Singing Tech
## Original Content
This dataset is created by the authors and encompasses two distinct singing styles: bel canto and Chinese folk singing. Bel canto is a vocal technique frequently employed in Western classical music and opera, symbolizing the zenith of vocal artistry within the broader Western musical heritage. Chinese folk singing, for which there is no official English translation, is referred to here as a classical singing style that originated in China during the 20th century. It is a fusion of traditional Chinese vocal techniques with European bel canto singing and is currently widely utilized in the performance of various forms of Chinese folk music. The purpose of creating this dataset is to fill a gap in the current singing datasets, as none of them includes Chinese folk singing, and by incorporating both bel canto and Chinese folk singing, it provides a valuable resource for researchers to conduct cross-cultural comparative studies in vocal performance. The original dataset contains 203 acapella singing recordings sung in two styles, bel canto and Chinese folk singing style. All of them were sung by professional vocalists and were recorded in the recording studio of the China Conservatory of Music using a Schoeps MK4 microphone. Additionally, apart from singing style labels, gender labels are also included.

## Integration
Since this is a self-created dataset, we directly carry out the unified integration of the data structure. After integration, the data structure of the dataset is as follows: audio (with a sampling rate of 22,050 Hz), mel spectrograms, 4-class numerical label, gender label and singing style label. The data number remains 203, with a total duration of 5.08 hours. The average duration is 90 seconds.

We have constructed the [default subset](#default-subset) of the current integrated version of the dataset, and its data structure can be viewed in the [viewer](https://huggingface.co/datasets/ccmusic-database/bel_canto/viewer). Since the default subset has not been evaluated, to verify its effectiveness, we have built the [eval subset](#eval-subset) 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://huggingface.co/ccmusic-database/bel_canto). Below are the data structures and invocation methods of the subsets.

## Statistics
| ![](https://www.modelscope.cn/datasets/ccmusic-database/bel_canto/resolve/master/data/bel_pie.jpg) | ![](https://www.modelscope.cn/datasets/ccmusic-database/bel_canto/resolve/master/data/bel_canto.jpg) | ![](https://www.modelscope.cn/datasets/ccmusic-database/bel_canto/resolve/master/data/bel_bar.jpg) |
| :------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------: |
|                                             **Fig. 1**                                             |                                              **Fig. 2**                                              |                                             **Fig. 3**                                             |

Firstly, **Fig. 1** presents the clip number of each category. The label with the highest data volume is Folk singing female, comprising 93 audio clips, which is 45.8% of the dataset. The label with the lowest data volume is Bel Canto Male, with 32 audio clips, constituting 15.8% of the dataset. Next, we assess the length of the audio for each label in **Fig. 2**. The Folk Singing Female label has the longest audio data, totaling 119.85 minutes. Bel Canto Male has the shortest audio duration, amounting to 46.61 minutes. The trend is the same as the data number difference shown in the pie chart. Lastly, in **Fig. 3**, the number of audio clips within various duration intervals is displayed. The most common duration range is observed to be 46-79 seconds, with 78 audio segments, while the least common range is 211-244 seconds, featuring only 2 audio segments.

|  Statistical items  |        Values        |
| :-----------------: | :------------------: |
|  Total duration(s)  | `18270.477865079374` |
|  Mean duration(s)   | `90.00235401516929`  |
|   Min duration(s)   |        `13.7`        |
|   Max duration(s)   |       `310.0`        |
| Class with max durs |  `Bel Canto Female`  |

### Data Splits
| Split(8:1:1) / Subset | default | eval  |
| :-------------------: | :-----: | :---: |
|         train         |   159   | 7926  |
|      validation       |   21    |  990  |
|         test          |   23    |  994  |
|         total         |   203   | 9910  |

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

### Default Subset Structure
<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>
</table>

### Eval Subset Structure
<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>
</table>

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

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

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

ds = load_dataset("ccmusic-database/bel_canto", split="train")
for item in ds:
    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_LFS_SKIP_SMUDGE=1 git clone [email protected]:datasets/ccmusic-database/bel_canto
cd bel_canto
```

## Dataset Description
### Dataset Summary
This dataset 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.

### 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

## 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 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