ccmusic-database/bel_canto

Dataset Card for Bel Conto and Chinese Folk Song Singing Tech

The raw dataset contains 203 acapella singing clips (sampled at 22,050 Hz) that are sung in two styles, Bel Conto and Chinese folk singing style by professional vocalists. All of them are sung by professional vocalists and were recorded in professional commercial recording studios. Besides the original version, the pre-processed version is included.

Viewer

https://www.modelscope.cn/datasets/ccmusic-database/bel_canto/dataPeview

Dataset Structure

Raw Subset

audio(.wav, 22050Hz)	mel(spectrogram, .jpg, 22050Hz)	label(4-class)	gender(2-class)	singing_method(2-class)
		m_bel, f_bel, m_folk, f_folk	male, female	Folk_Singing, Bel_Canto
...	...	...	...	...

Eval Subset

mel (.jpg, 1.6s, 48000Hz)	cqt (.jpg, 1.6s, 48000Hz)	chroma (.jpg, 1.6s, 48000Hz)	label (4-class)	gender (2-class)	singing_method (2-class)
			m_bel, f_bel, m_folk, f_folk	male, female	Folk_Singing, Bel_Canto
...	...	...	...	...	...

Data Instances

.zip(.wav, .jpg)

Data Fields

m_bel, f_bel, m_folk, f_folk

Data Splits

Split	Raw	Eval
total	203	9603
train(80%)	162	7682
validation(10%)	20	960
test(10%)	21	961

Usage

Raw Subset

from datasets import load_dataset

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

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

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

Eval Subset

from datasets import load_dataset

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

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

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

Maintenance

GIT_LFS_SKIP_SMUDGE=1 git clone git@hf.co:datasets/ccmusic-database/bel_canto
cd bel_canto

Dataset Summary

This database contains hundreds of acapella singing clips that are sung in two styles, Bel Conto and Chinese national singing style by professional vocalists. All of them are sung by professional vocalists and were recorded in professional commercial recording studios.

Supported Tasks and Leaderboards

Audio classification, Image classification, singing method classification, voice classification

Languages

Chinese, English

Dataset Creation

Curation Rationale

Lack of a dataset for Bel Conto and Chinese folk song singing tech

Source Data

Initial Data Collection and Normalization

Zhaorui Liu, Monan Zhou

Who are the source language producers?

Students from CCMUSIC

Annotations

Annotation process

All of them are sung by professional vocalists and were recorded in professional commercial recording studios.

Who are the annotators?

professional vocalists

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 Chinese songs

Other Known Limitations

Some singers may not have enough professional training in classical or ethnic vocal techniques.

Additional Information

Dataset Curators

Zijin Li

Evaluation

https://huggingface.co/ccmusic-database/bel_canto

Citation Information

@dataset{zhaorui_liu_2021_5676893,
  author       = {Monan Zhou, Shenyang Xu, Zhaorui Liu, Zhaowen Wang, Feng Yu, Wei Li and Baoqiang Han},
  title        = {CCMusic: an Open and Diverse Database for Chinese and General Music Information Retrieval Research},
  month        = {mar},
  year         = {2024},
  publisher    = {HuggingFace},
  version      = {1.2},
  url          = {https://huggingface.co/ccmusic-database}
}

Contributions

Provide a dataset for distinguishing Bel Conto and Chinese folk song singing tech