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Dataset Card for Chest voice and Falsetto Dataset
The raw dataset comprises 1,280 monophonic singing audio files in .wav format (sample rate is 22,050 Hz), consisting of chest and falsetto voices performed, recorded, and annotated by students majoring in Vocal Music at the China Conservatory of Music. The chest voice is tagged as chest and the falsetto voice is tagged as falsetto. Additionally, the dataset includes the Mel spectrogram, Mel frequency cepstral coefficient (MFCC), and spectral characteristics of each audio segment, resulting in a total of 5,120 CSV files. The original dataset did not differentiate between male and female voices, an omission that is critical for accurately identifying chest and falsetto vocal techniques. To address this, we conducted a meticulous manual review and added gender annotations to the dataset. Besides the original content, the preprocessed version during the evaluation which will be detailed in section IV is also provided. This approach which provides two versions is applied to the two subsequent classification datasets that have not been evaluated as well: Music Genre Dataset, Bel Conto & Chinese Folk Singing Dataset.
Viewer
https://www.modelscope.cn/datasets/ccmusic-database/chest_falsetto/dataPeview
Dataset Structure
Raw Subset
audio(.wav, 22050Hz) | mel(spectrogram, .jpg, 22050Hz) | label(4-class) | gender(2-class) | singing_method(2-class) |
---|---|---|---|---|
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m_chest, m_falsetto, f_chest, f_falsetto | male, female | chest, falsetto | |
... | ... | ... | ... | ... |
Eval Subset
mel(.jpg, 0.07s, 48000Hz) | cqt(.jpg, 0.07s, 48000Hz) | chroma(.jpg, 0.07s, 48000Hz) | label | gender | singing_method |
---|---|---|---|---|---|
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m_chest, m_falsetto, f_chest, f_falsetto | male, female | chest, falsetto |
... | ... | ... | ... | ... | ... |
Data Instances
.zip(.wav, .jpg)
Data Fields
m_chest, f_chest, m_falsetto, f_falsetto
Data Splits
Split | Eval | Raw |
---|---|---|
total | 8974 | 1280 |
train(80%) | 7179 | 1024 |
validation(10%) | 897 | 128 |
test(10%) | 898 | 128 |
Usage
Raw Subset
from datasets import load_dataset
ds = load_dataset("ccmusic-database/chest_falsetto", 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/chest_falsetto", 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/chest_falsetto
cd chest_falsetto
Dataset Summary
For the pre-processed version, the audio clip was into 0.25 seconds and then transformed to Mel, CQT and Chroma spectrogram in .jpg format, resulting in 8,974 files. The chest/falsetto label for each file is given as one of the four classes: m chest, m falsetto, f chest, and f falsetto. The spectrogram, the chest/falsetto label and the gender label are combined into one data entry, with the first three columns representing the Mel, CQT and Chroma. The fourth and fifth columns are the chest/falsetto label and gender label, respectively. Additionally, the integrated dataset provides the function to shuffle and split the dataset into training, validation, and test sets in an 8:1:1 ratio. This dataset can be used for singing-related tasks such as singing gender classification or chest and falsetto voice classification.
Supported Tasks and Leaderboards
Audio classification, singing method classification, voice classification
Languages
Chinese, English
Dataset Creation
Curation Rationale
Lack of a dataset for Chest voice and Falsetto
Source Data
Initial Data Collection and Normalization
Zhaorui Liu, Monan Zhou
Who are the source language producers?
Students from CCMUSIC
Annotations
Annotation process
1280 monophonic singing audio (.wav format) of chest and falsetto voices, with chest voice tagged as chest and falsetto voice tagged as falsetto.
Who are the annotators?
Students from CCMUSIC
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 chest and falsetto voices
Other Known Limitations
Recordings are cut into slices that are too short; The CQT spectrum column has the problem of spectrum leakage, but because the original audio slice is too short, only 0.5s, it cannot effectively avoid this problem.
Additional Information
Dataset Curators
Zijin Li
Evaluation
https://huggingface.co/ccmusic-database/chest_falsetto
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 chest and falsetto voices
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Models trained or fine-tuned on ccmusic-database/chest_falsetto
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