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 [email protected]: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
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