---
license: mit
task_categories:
- audio-classification
language:
- zh
- en
tags:
- music
- art
pretty_name: Timbre and Range Dataset
size_categories:
- 1K<n<10K
viewer: false
---

# Dataset Card for Timbre and Range Dataset
## Dataset Summary
The timbre dataset contains acapella singing audio of 9 singers, as well as cut single-note audio, totaling 775 clips (.wav format)
The vocal range dataset 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
<https://www.modelscope.cn/datasets/ccmusic-database/timbre_range/dataPeview>
### Data Instances
.zip(.wav, .jpg), .csv

### Data Fields
```txt
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

## Usage
### Timbre subset
```python
from datasets import load_dataset

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

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

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

### Range subset
```python
from datasets import load_dataset
# default subset
ds = load_dataset("ccmusic-database/timbre_range", name="range")
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 git@hf.co:datasets/ccmusic-database/timbre_range
cd timbre_range
```

## 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

## Mirror
<https://www.modelscope.cn/datasets/ccmusic-database/timbre_range>

## Additional Information
### Dataset Curators
Zijin Li

### Evaluation
[1] [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)

### Citation Information
```bibtex
@article{2019Data,
  title={Data Augmentation based Convolutional Neural Network for Auscultation},
  author={ Yiliang, Jiang  and  Xulong, Zhang  and  Jin, Deng  and  Wenqiang, Zhang  and  Wei, L. I. },
  journal={Journal of Fudan University(Natural Science)},
  year={2019},
}
```

### Contributions
Provide a dataset for music timbre and range