# Dataset Card for Timbre and Range Database
## Dataset Description
- **Homepage:** <https://ccmusic-database.github.io>
- **Repository:** <https://huggingface.co/datasets/ccmusic-database/timbre_score>
- **Paper:** <https://doi.org/10.5281/zenodo.5676893>
- **Leaderboard:** <https://ccmusic-database.github.io/team.html>
- **Point of Contact:** N/A

### Dataset Summary
The timbre database contains acapella singing audio of 9 singers, as well as cut single-note audio, totaling 775 clips (.wav format)
The vocal range database includes several up and down chromatic scales audio clips of several vocals, as well as the cut single-note audio clips (.wav format).

### Supported Tasks and Leaderboards
Audio classification

### Languages
Chinese, English

## Dataset Structure
### Data Instances
.zip(.wav, .jpg), .csv

### Data Fields
```
timbre: song1-32
range: vox1_19-22/26-29/32/33/36-38/41-47/51-55/59-64/69-71/79-81
```

### Data Splits
Train, Validation, Test

## Dataset Creation
### Curation Rationale
Promoting the development of music AI industry

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

#### Who are the source language producers?
Composers of the songs in dataset

### Annotations
#### Annotation process
CCMUSIC students collected acapella singing audios of 9 singers, as well as cut single-note audio, totaling 775 clips

#### Who are the annotators?
Students from CCMUSIC

### Personal and Sensitive Information
Due to copyright issues with the original music, only acapella singing audios are provided in the dataset

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

### Discussion of Biases
Most are Chinese songs

### Other Known Limitations
Samples are not balanced enough

## Additional Information
### Dataset Curators
Zijin Li

### Evaluation
[Yiliang, J. et al. (2019) ‘Data Augmentation based Convolutional Neural Network for Auscultation’, Journal of Fudan University(Natural Science), pp. 328–334. doi:10.15943/j.cnki.fdxb-jns.2019.03.004.](https://kns.cnki.net/kcms/detail/detail.aspx?dbcode=CJFD&dbname=CJFDLAST2019&filename=FDXB201903004&uniplatform=NZKPT&v=VAszHDtjPUYMi3JYVrdSGx4fcqlEtgCeKwRGTacCj98CGEQg5CUFHxakrvuaMzm3)

### Licensing Information
```
MIT License

Copyright (c) CCMUSIC

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
```

### Citation Information
```
@dataset{zhaorui_liu_2021_5676893,
  author       = {Zhaorui Liu, Monan Zhou, Shenyang Xu, Yuan Wang, Zhaowen Wang, Wei Li and Zijin Li},
  title        = {CCMUSIC DATABASE: A Music Data Sharing Platform for Computational Musicology Research},
  month        = {nov},
  year         = {2021},
  publisher    = {Zenodo},
  version      = {1.1},
  doi          = {10.5281/zenodo.5676893},
  url          = {https://doi.org/10.5281/zenodo.5676893}
}
```

### Contributions
Provide a dataset for music timbre and range
task_categories:audio-classification size_categories:1K<n<10K language:zh language:en license:mit music art region:us
{"language": ["zh", "en"], "license": "mit", "size_categories": ["1K<n<10K"], "task_categories": ["audio-classification"], "pretty_name": "Timbre and Range Database", "tags": ["music", "art"], "viewer": false}
TAGS
#task_categories-audio-classification #size_categories-1K<n<10K #language-Chinese #language-English #license-mit #music #art #region-us 

# Dataset Card for Timbre and Range Database
## Dataset Description
- Homepage: <URL>
- Repository: <URL
- Paper: <URL
- Leaderboard: <URL
- Point of Contact: N/A

### Dataset Summary
The timbre database contains acapella singing audio of 9 singers, as well as cut single-note audio, totaling 775 clips (.wav format)
The vocal range database includes several up and down chromatic scales audio clips of several vocals, as well as the cut single-note audio clips (.wav format).

### Supported Tasks and Leaderboards
Audio classification

### Languages
Chinese, English

## Dataset Structure
### Data Instances
.zip(.wav, .jpg), .csv

### Data Fields


### Data Splits
Train, Validation, Test

## Dataset Creation
### Curation Rationale
Promoting the development of music AI industry

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

#### Who are the source language producers?
Composers of the songs in dataset

### Annotations
#### Annotation process
CCMUSIC students collected acapella singing audios of 9 singers, as well as cut single-note audio, totaling 775 clips

#### Who are the annotators?
Students from CCMUSIC

### Personal and Sensitive Information
Due to copyright issues with the original music, only acapella singing audios are provided in the dataset

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

### Discussion of Biases
Most are Chinese songs

### Other Known Limitations
Samples are not balanced enough

## Additional Information
### Dataset Curators
Zijin Li

### Evaluation
Yiliang, J. et al. (2019) ‘Data Augmentation based Convolutional Neural Network for Auscultation’, Journal of Fudan University(Natural Science), pp. 328–334. doi:10.15943/j.URL-jns.2019.03.004.

### Licensing Information





### Contributions
Provide a dataset for music timbre and range
# Dataset Card for Timbre and Range Database ## Dataset Description
- Homepage: <URL>
- Repository: <URL
- Paper: <URL
- Leaderboard: <URL
- Point of Contact: N/A ### Dataset Summary
The timbre database contains acapella singing audio of 9 singers, as well as cut single-note audio, totaling 775 clips (.wav format)
The vocal range database includes several up and down chromatic scales audio clips of several vocals, as well as the cut single-note audio clips (.wav format). ### Supported Tasks and Leaderboards
Audio classification ### Languages
Chinese, English ## Dataset Structure ### Data Instances
.zip(.wav, .jpg), .csv ### Data Fields ### Data Splits
Train, Validation, Test ## Dataset Creation ### Curation Rationale
Promoting the development of music AI industry ### Source Data #### Initial Data Collection and Normalization
Zijin Li, Zhaorui Liu, Monan Zhou #### Who are the source language producers?
Composers of the songs in dataset ### Annotations #### Annotation process
CCMUSIC students collected acapella singing audios of 9 singers, as well as cut single-note audio, totaling 775 clips #### Who are the annotators?
Students from CCMUSIC ### Personal and Sensitive Information
Due to copyright issues with the original music, only acapella singing audios are provided in the dataset ## Considerations for Using the Data ### Social Impact of Dataset
Promoting the development of AI in the music industry ### Discussion of Biases
Most are Chinese songs ### Other Known Limitations
Samples are not balanced enough ## Additional Information ### Dataset Curators
Zijin Li ### Evaluation
Yiliang, J. et al. (2019) ‘Data Augmentation based Convolutional Neural Network for Auscultation’, Journal of Fudan University(Natural Science), pp. 328–334. doi:10.15943/j.URL-jns.2019.03.004. ### Licensing Information ### Contributions
Provide a dataset for music timbre and range
TAGS
#task_categories-audio-classification #size_categories-1K<n<10K #language-Chinese #language-English #license-mit #music #art #region-us 
 # Dataset Card for Timbre and Range Database ## Dataset Description
- Homepage: <URL>
- Repository: <URL
- Paper: <URL
- Leaderboard: <URL
- Point of Contact: N/A ### Dataset Summary
The timbre database contains acapella singing audio of 9 singers, as well as cut single-note audio, totaling 775 clips (.wav format)
The vocal range database includes several up and down chromatic scales audio clips of several vocals, as well as the cut single-note audio clips (.wav format). ### Supported Tasks and Leaderboards
Audio classification ### Languages
Chinese, English ## Dataset Structure ### Data Instances
.zip(.wav, .jpg), .csv ### Data Fields ### Data Splits
Train, Validation, Test ## Dataset Creation ### Curation Rationale
Promoting the development of music AI industry ### Source Data #### Initial Data Collection and Normalization
Zijin Li, Zhaorui Liu, Monan Zhou #### Who are the source language producers?
Composers of the songs in dataset ### Annotations #### Annotation process
CCMUSIC students collected acapella singing audios of 9 singers, as well as cut single-note audio, totaling 775 clips #### Who are the annotators?
Students from CCMUSIC ### Personal and Sensitive Information
Due to copyright issues with the original music, only acapella singing audios are provided in the dataset ## Considerations for Using the Data ### Social Impact of Dataset
Promoting the development of AI in the music industry ### Discussion of Biases
Most are Chinese songs ### Other Known Limitations
Samples are not balanced enough ## Additional Information ### Dataset Curators
Zijin Li ### Evaluation
Yiliang, J. et al. (2019) ‘Data Augmentation based Convolutional Neural Network for Auscultation’, Journal of Fudan University(Natural Science), pp. 328–334. doi:10.15943/j.URL-jns.2019.03.004. ### Licensing Information ### Contributions
Provide a dataset for music timbre and range
passage: TAGS
#task_categories-audio-classification #size_categories-1K<n<10K #language-Chinese #language-English #license-mit #music #art #region-us 
# Dataset Card for Timbre and Range Database## Dataset Description
- Homepage: <URL>
- Repository: <URL
- Paper: <URL
- Leaderboard: <URL
- Point of Contact: N/A### Dataset Summary
The timbre database contains acapella singing audio of 9 singers, as well as cut single-note audio, totaling 775 clips (.wav format)
The vocal range database includes several up and down chromatic scales audio clips of several vocals, as well as the cut single-note audio clips (.wav format).### Supported Tasks and Leaderboards
Audio classification### Languages
Chinese, English## Dataset Structure### Data Instances
.zip(.wav, .jpg), .csv### Data Fields### Data Splits
Train, Validation, Test## Dataset Creation### Curation Rationale
Promoting the development of music AI industry### Source Data#### Initial Data Collection and Normalization
Zijin Li, Zhaorui Liu, Monan Zhou#### Who are the source language producers?
Composers of the songs in dataset### Annotations#### Annotation process
CCMUSIC students collected acapella singing audios of 9 singers, as well as cut single-note audio, totaling 775 clips#### Who are the annotators?
Students from CCMUSIC### Personal and Sensitive Information
Due to copyright issues with the original music, only acapella singing audios are provided in the dataset## Considerations for Using the Data### Social Impact of Dataset
Promoting the development of AI in the music industry### Discussion of Biases
Most are Chinese songs### Other Known Limitations
Samples are not balanced enough## Additional Information### Dataset Curators
Zijin Li