ccmusic-database/timbre_range

1---2license: mit3task_categories:4- audio-classification5language:6- zh7- en8tags:9- music10- art11pretty_name: Timbre and Range Dataset12size_categories:13- 1K<n<10K14dataset_info:15  - config_name: timbre16    features:17      - name: audio18        dtype:19          audio:20            sampling_rate: 4410021      - name: mel22        dtype: image23      - name: label24        dtype: 25          class_label:26            names:27                '0': Base28                '1': Split29                '2': Short30      - name: score131        dtype: float6432      - name: score233        dtype: float6434      - name: avg_score35        dtype: float6436    splits:37      - name: train38        num_bytes: 21364439        num_examples: 53740      - name: validation41        num_bytes: 2666442        num_examples: 6743      - name: test44        num_bytes: 2708845        num_examples: 6846    download_size: 59542592147    dataset_size: 26739648  - config_name: range49    features:50      - name: audio51        dtype:52          audio:53            sampling_rate: 4410054      - name: mel55        dtype: image56      - name: label57        dtype:58          class_label:59            names:60                '0': Narrow61                '1': Moderate62                '2': Wide63    splits:64      - name: train65        num_bytes: 21005266        num_examples: 58067      - name: validation68        num_bytes: 2646269        num_examples: 7370      - name: test71        num_bytes: 2640072        num_examples: 7373    download_size: 6530916474    dataset_size: 26291475configs:76  - config_name: timbre77    data_files:78      - split: train79        path: timbre/train/data-*.arrow80      - split: validation81        path: timbre/validation/data-*.arrow82      - split: test83        path: timbre/test/data-*.arrow84  - config_name: range85    data_files:86      - split: train87        path: range/train/data-*.arrow88      - split: validation89        path: range/validation/data-*.arrow90      - split: test91        path: range/test/data-*.arrow92---93 94# Dataset Card for Timbre and Range Dataset95## Dataset Summary96The timbre dataset contains acapella singing audio of 9 singers, as well as cut single-note audio, totaling 775 clips (.wav format)97The 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).98 99### Supported Tasks and Leaderboards100Audio classification101 102### Languages103Chinese, English104 105## Dataset Structure106<https://huggingface.co/datasets/ccmusic-database/timbre_range/viewer>107 108### Data Instances109.zip(.wav, .jpg), .csv110 111### Data Fields112```txt113timbre: song1-32114range: vox1_19-22/26-29/32/33/36-38/41-47/51-55/59-64/69-71/79-81115```116 117### Data Splits118Train, Validation, Test119 120## Usage121```python122from datasets import load_dataset123 124ds = load_dataset(125    "ccmusic-database/timbre_range",126    name="timbre",  # timbre / range127    split="train",  # train / validation / test128    cache_dir="./__pycache__",129)130for i in ds:131    print(i)132```133 134## Maintenance135```bash136GIT_LFS_SKIP_SMUDGE=1 git clone git@hf.co:datasets/ccmusic-database/timbre_range137cd timbre_range138```139 140## Mirror141<https://www.modelscope.cn/datasets/ccmusic-database/timbre_range>142 143## Dataset Creation144### Curation Rationale145Promoting the development of music AI industry146 147### Source Data148#### Initial Data Collection and Normalization149Zijin Li, Zhaorui Liu, Monan Zhou150 151#### Who are the source language producers?152Composers of the songs in dataset153 154### Annotations155#### Annotation process156CCMUSIC students collected acapella singing audios of 9 singers, as well as cut single-note audio, totaling 775 clips157 158#### Who are the annotators?159Students from CCMUSIC160 161### Personal and Sensitive Information162Due to copyright issues with the original music, only acapella singing audios are provided in the dataset163 164## Considerations for Using the Data165### Social Impact of Dataset166Promoting the development of AI in the music industry167 168### Discussion of Biases169Most are Chinese songs170 171### Other Known Limitations172Samples are not balanced enough173 174## Additional Information175### Dataset Curators176Zijin Li177 178### Evaluation179[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)180 181### Citation Information182```bibtex183@article{2019Data,184  title={Data Augmentation based Convolutional Neural Network for Auscultation},185  author={Yiliang Jiang and Xulong Zhang and Jin Deng and Wenqiang Zhang and Wei Li},186  journal={Journal of Fudan University (Natural Science)},187  year={2019},188}189```190 191### Contributions192Provide a dataset for music timbre and range