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

### Dataset Summary
This database contains subjective timbre evaluation scores of 16 subjective timbre evaluation terms (such as bright, dark, raspy) on 37 Chinese national and 24 non-Chinese terms given by 14 participants in a subjective evaluation experiment.

### Supported Tasks and Leaderboards
Musical Instruments Timbre Evaluation

### Languages
Chinese, English

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

### Data Fields
Traditional instruments

### Data Splits
Chinese, Non-Chinese

## Dataset Creation
### Curation Rationale
Lack of a dataset for musical instruments timbre evaluation

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

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

### Annotations
#### Annotation process
Subjective timbre evaluation scores of 16 subjective timbre evaluation terms (such as bright, dark, raspy) on 37 Chinese national and 24 Non-Chinese terms given by 14 participants in a subjective evaluation experiment

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

### 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 traditional instruments

### Other Known Limitations
Limited data

## Additional Information
### Dataset Curators
Zijin Li

### Evaluation
#### For Chinese instruments
[Yiliang, J. et al. (2020) ‘Analysis of Chinese Musical Instrument Timbre Based on Objective Features’, Journal of Fudan University(Natural Science), pp. 346-353+359. doi:10.15943/j.cnki.fdxb-jns.2020.03.014.](https://kns.cnki.net/kcms/detail/detail.aspx?dbcode=CJFD&dbname=CJFDLAST2020&filename=FDXB202003014&uniplatform=NZKPT&v=85qLeLUyrDt%25mmd2Btak%25mmd2BN90N7vYZSv%25mmd2BVc1EfPmaYcvpvrgY1XkL215gYG4J%25mmd2FD09viR0w)
#### For Non-Chinese instruments
[Jiang, Wei et al. “Analysis and Modeling of Timbre Perception Features of Chinese Musical Instruments.” 2019 IEEE/ACIS 18th International Conference on Computer and Information Science (ICIS) (2019): 191-195.](https://ieeexplore.ieee.org/document/8940168)

### 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 musical instruments timbre evaluation
task_categories:audio-classification size_categories:n<1K language:zh language:en license:mit music art region:us
{"language": ["zh", "en"], "license": "mit", "size_categories": ["n<1K"], "task_categories": ["audio-classification"], "pretty_name": "Musical Instruments Timbre Evaluation Database", "tags": ["music", "art"], "viewer": false}
TAGS
#task_categories-audio-classification #size_categories-n<1K #language-Chinese #language-English #license-mit #music #art #region-us 

# Dataset Card for Chinese Musical Instruments Timbre Evaluation Database
## Dataset Description
- Homepage: <URL>
- Repository: <URL
- Paper: <URL
- Leaderboard: <URL
- Point of Contact: N/A

### Dataset Summary
This database contains subjective timbre evaluation scores of 16 subjective timbre evaluation terms (such as bright, dark, raspy) on 37 Chinese national and 24 non-Chinese terms given by 14 participants in a subjective evaluation experiment.

### Supported Tasks and Leaderboards
Musical Instruments Timbre Evaluation

### Languages
Chinese, English

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

### Data Fields
Traditional instruments

### Data Splits
Chinese, Non-Chinese

## Dataset Creation
### Curation Rationale
Lack of a dataset for musical instruments timbre evaluation

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

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

### Annotations
#### Annotation process
Subjective timbre evaluation scores of 16 subjective timbre evaluation terms (such as bright, dark, raspy) on 37 Chinese national and 24 Non-Chinese terms given by 14 participants in a subjective evaluation experiment

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

### 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 traditional instruments

### Other Known Limitations
Limited data

## Additional Information
### Dataset Curators
Zijin Li

### Evaluation
#### For Chinese instruments
Yiliang, J. et al. (2020) ‘Analysis of Chinese Musical Instrument Timbre Based on Objective Features’, Journal of Fudan University(Natural Science), pp. 346-353+359. doi:10.15943/j.URL-jns.2020.03.014.
#### For Non-Chinese instruments
Jiang, Wei et al. “Analysis and Modeling of Timbre Perception Features of Chinese Musical Instruments.” 2019 IEEE/ACIS 18th International Conference on Computer and Information Science (ICIS) (2019): 191-195.

### Licensing Information





### Contributions
Provide a dataset for musical instruments timbre evaluation
# Dataset Card for Chinese Musical Instruments Timbre Evaluation Database ## Dataset Description
- Homepage: <URL>
- Repository: <URL
- Paper: <URL
- Leaderboard: <URL
- Point of Contact: N/A ### Dataset Summary
This database contains subjective timbre evaluation scores of 16 subjective timbre evaluation terms (such as bright, dark, raspy) on 37 Chinese national and 24 non-Chinese terms given by 14 participants in a subjective evaluation experiment. ### Supported Tasks and Leaderboards
Musical Instruments Timbre Evaluation ### Languages
Chinese, English ## Dataset Structure ### Data Instances
.zip(.wav), .csv ### Data Fields
Traditional instruments ### Data Splits
Chinese, Non-Chinese ## Dataset Creation ### Curation Rationale
Lack of a dataset for musical instruments timbre evaluation ### Source Data #### Initial Data Collection and Normalization
Zhaorui Liu, Monan Zhou #### Who are the source language producers?
Students from CCMUSIC ### Annotations #### Annotation process
Subjective timbre evaluation scores of 16 subjective timbre evaluation terms (such as bright, dark, raspy) on 37 Chinese national and 24 Non-Chinese terms given by 14 participants in a subjective evaluation experiment #### Who are the annotators?
Students from CCMUSIC ### 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 traditional instruments ### Other Known Limitations
Limited data ## Additional Information ### Dataset Curators
Zijin Li ### Evaluation #### For Chinese instruments
Yiliang, J. et al. (2020) ‘Analysis of Chinese Musical Instrument Timbre Based on Objective Features’, Journal of Fudan University(Natural Science), pp. 346-353+359. doi:10.15943/j.URL-jns.2020.03.014. #### For Non-Chinese instruments
Jiang, Wei et al. “Analysis and Modeling of Timbre Perception Features of Chinese Musical Instruments.” 2019 IEEE/ACIS 18th International Conference on Computer and Information Science (ICIS) (2019): 191-195. ### Licensing Information ### Contributions
Provide a dataset for musical instruments timbre evaluation
TAGS
#task_categories-audio-classification #size_categories-n<1K #language-Chinese #language-English #license-mit #music #art #region-us 
 # Dataset Card for Chinese Musical Instruments Timbre Evaluation Database ## Dataset Description
- Homepage: <URL>
- Repository: <URL
- Paper: <URL
- Leaderboard: <URL
- Point of Contact: N/A ### Dataset Summary
This database contains subjective timbre evaluation scores of 16 subjective timbre evaluation terms (such as bright, dark, raspy) on 37 Chinese national and 24 non-Chinese terms given by 14 participants in a subjective evaluation experiment. ### Supported Tasks and Leaderboards
Musical Instruments Timbre Evaluation ### Languages
Chinese, English ## Dataset Structure ### Data Instances
.zip(.wav), .csv ### Data Fields
Traditional instruments ### Data Splits
Chinese, Non-Chinese ## Dataset Creation ### Curation Rationale
Lack of a dataset for musical instruments timbre evaluation ### Source Data #### Initial Data Collection and Normalization
Zhaorui Liu, Monan Zhou #### Who are the source language producers?
Students from CCMUSIC ### Annotations #### Annotation process
Subjective timbre evaluation scores of 16 subjective timbre evaluation terms (such as bright, dark, raspy) on 37 Chinese national and 24 Non-Chinese terms given by 14 participants in a subjective evaluation experiment #### Who are the annotators?
Students from CCMUSIC ### 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 traditional instruments ### Other Known Limitations
Limited data ## Additional Information ### Dataset Curators
Zijin Li ### Evaluation #### For Chinese instruments
Yiliang, J. et al. (2020) ‘Analysis of Chinese Musical Instrument Timbre Based on Objective Features’, Journal of Fudan University(Natural Science), pp. 346-353+359. doi:10.15943/j.URL-jns.2020.03.014. #### For Non-Chinese instruments
Jiang, Wei et al. “Analysis and Modeling of Timbre Perception Features of Chinese Musical Instruments.” 2019 IEEE/ACIS 18th International Conference on Computer and Information Science (ICIS) (2019): 191-195. ### Licensing Information ### Contributions
Provide a dataset for musical instruments timbre evaluation
passage: TAGS
#task_categories-audio-classification #size_categories-n<1K #language-Chinese #language-English #license-mit #music #art #region-us 
# Dataset Card for Chinese Musical Instruments Timbre Evaluation Database## Dataset Description
- Homepage: <URL>
- Repository: <URL
- Paper: <URL
- Leaderboard: <URL
- Point of Contact: N/A### Dataset Summary
This database contains subjective timbre evaluation scores of 16 subjective timbre evaluation terms (such as bright, dark, raspy) on 37 Chinese national and 24 non-Chinese terms given by 14 participants in a subjective evaluation experiment.### Supported Tasks and Leaderboards
Musical Instruments Timbre Evaluation### Languages
Chinese, English## Dataset Structure### Data Instances
.zip(.wav), .csv### Data Fields
Traditional instruments### Data Splits
Chinese, Non-Chinese## Dataset Creation### Curation Rationale
Lack of a dataset for musical instruments timbre evaluation### Source Data#### Initial Data Collection and Normalization
Zhaorui Liu, Monan Zhou#### Who are the source language producers?
Students from CCMUSIC### Annotations#### Annotation process
Subjective timbre evaluation scores of 16 subjective timbre evaluation terms (such as bright, dark, raspy) on 37 Chinese national and 24 Non-Chinese terms given by 14 participants in a subjective evaluation experiment#### Who are the annotators?
Students from CCMUSIC### 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 traditional instruments### Other Known Limitations
Limited data## Additional Information### Dataset Curators
Zijin Li### Evaluation