ccmusic-database/bel_canto

Dataset Card for Bel Conto and Chinese Folk Song Singing Tech

The original dataset, sourced from the Bel Canto and National Singing Dataset, contains 203 acapella singing clips performed in two styles, Bel Canto and Chinese folk singing style, by professional vocalists. All of them are sung by professional vocalists and were recorded in professional commercial recording studios.

Based on the aforementioned original dataset, we have constructed the default subset of the current integrated version of the dataset, and its data structure can be viewed in the viewer. Since the default subset has not been evaluated, to verify its effectiveness, we have built the eval subset based on the default subset for the evaluation of the integrated version of the dataset. The evaluation results can be seen in the bel_canto. Below are the data structures and invocation methods of the subsets.

Dataset Structure

Default Subset

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

Eval Subset

mel	cqt	chroma	label (4-class)	gender (2-class)	singing_method (2-class)
.jpg, 1.6s, 22050Hz	.jpg, 1.6s, 22050Hz	.jpg, 1.6s, 22050Hz	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(8:1:1) / Subset	default	eval
train	159	7907
validation	21	988
test	23	991
total	203	9886
total duration(s)	18192.37652721089	18192.37652721089

Viewer

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

Usage

Default Subset

from datasets import load_dataset

ds = 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

ds = 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 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