README.md

---
license: mit
task_categories:
- audio-classification
language:
- zh
- en
tags:
- music
- art
pretty_name: Timbre and Range Dataset
size_categories:
- 1K<n<10K
dataset_info:
  - config_name: timbre
    features:
      - name: audio
        dtype:
          audio:
            sampling_rate: 44100
      - name: mel
        dtype: image
      - name: label
        dtype: 
          class_label:
            names:
                '0': Base
                '1': Split
                '2': Short
      - name: score1
        dtype: float64
      - name: score2
        dtype: float64
      - name: avg_score
        dtype: float64
    splits:
      - name: train
        num_bytes: 213644
        num_examples: 537
      - name: validation
        num_bytes: 26664
        num_examples: 67
      - name: test
        num_bytes: 27088
        num_examples: 68
    download_size: 595425921
    dataset_size: 267396
  - config_name: range
    features:
      - name: audio
        dtype:
          audio:
            sampling_rate: 44100
      - name: mel
        dtype: image
      - name: label
        dtype:
          class_label:
            names:
                '0': Narrow
                '1': Moderate
                '2': Wide
    splits:
      - name: train
        num_bytes: 210052
        num_examples: 580
      - name: validation
        num_bytes: 26462
        num_examples: 73
      - name: test
        num_bytes: 26400
        num_examples: 73
    download_size: 65309164
    dataset_size: 262914
configs:
  - config_name: timbre
    data_files:
      - split: train
        path: timbre/train/data-*.arrow
      - split: validation
        path: timbre/validation/data-*.arrow
      - split: test
        path: timbre/test/data-*.arrow
  - config_name: range
    data_files:
      - split: train
        path: range/train/data-*.arrow
      - split: validation
        path: range/validation/data-*.arrow
      - split: test
        path: range/test/data-*.arrow
---

# 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://huggingface.co/datasets/ccmusic-database/timbre_range/viewer>

### 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

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_LFS_SKIP_SMUDGE=1 git clone [email protected]:datasets/ccmusic-database/timbre_range
cd timbre_range
```

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

## 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 Li},
  journal={Journal of Fudan University (Natural Science)},
  year={2019},
}
```

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