Datasets:
license: mit
task_categories:
- audio-classification
language:
- zh
- en
tags:
- music
- art
pretty_name: Timbre and Range Dataset
size_categories:
- 1K<n<10K
dataset_info:
- config_name: timbre
features:
- name: audio
dtype:
audio:
sampling_rate: 44100
- name: mel
dtype: image
- name: label
dtype:
class_label:
names:
'0': Base
'1': Split
'2': Short
- name: score1
dtype: float64
- name: score2
dtype: float64
- name: avg_score
dtype: float64
splits:
- name: train
num_bytes: 213644
num_examples: 537
- name: validation
num_bytes: 26664
num_examples: 67
- name: test
num_bytes: 27088
num_examples: 68
download_size: 595425921
dataset_size: 267396
- config_name: range
features:
- name: audio
dtype:
audio:
sampling_rate: 44100
- name: mel
dtype: image
- name: label
dtype:
class_label:
names:
'0': Narrow
'1': Moderate
'2': Wide
splits:
- name: train
num_bytes: 210052
num_examples: 580
- name: validation
num_bytes: 26462
num_examples: 73
- name: test
num_bytes: 26400
num_examples: 73
download_size: 65309164
dataset_size: 262914
configs:
- config_name: timbre
data_files:
- split: train
path: timbre/train/data-*.arrow
- split: validation
path: timbre/validation/data-*.arrow
- split: test
path: timbre/test/data-*.arrow
- config_name: range
data_files:
- split: train
path: range/train/data-*.arrow
- split: validation
path: range/validation/data-*.arrow
- split: test
path: range/test/data-*.arrow
Dataset Card for Timbre and Range Dataset
Dataset Summary
The timbre dataset contains acapella singing audio of 9 singers, as well as cut single-note audio, totaling 775 clips (.wav format) The 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).
Supported Tasks and Leaderboards
Audio classification
Languages
Chinese, English
Dataset Structure
https://huggingface.co/datasets/ccmusic-database/timbre_range/viewer
Data Instances
.zip(.wav, .jpg), .csv
Data Fields
timbre: song1-32
range: vox1_19-22/26-29/32/33/36-38/41-47/51-55/59-64/69-71/79-81
Data Splits
Train, Validation, Test
Usage
Timbre subset
from datasets import load_dataset
ds = load_dataset("ccmusic-database/timbre_range", name="timbre")
for item in ds["train"]:
print(item)
for item in ds["validation"]:
print(item)
for item in ds["test"]:
print(item)
Range subset
from datasets import load_dataset
ds = load_dataset("ccmusic-database/timbre_range", name="range")
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 [email protected]:datasets/ccmusic-database/timbre_range
cd timbre_range
Mirror
https://www.modelscope.cn/datasets/ccmusic-database/timbre_range
Dataset Creation
Curation Rationale
Promoting the development of music AI industry
Source Data
Initial Data Collection and Normalization
Zijin Li, Zhaorui Liu, Monan Zhou
Who are the source language producers?
Composers of the songs in dataset
Annotations
Annotation process
CCMUSIC students collected acapella singing audios of 9 singers, as well as cut single-note audio, totaling 775 clips
Who are the annotators?
Students from CCMUSIC
Personal and Sensitive Information
Due to copyright issues with the original music, only acapella singing audios are provided in the dataset
Considerations for Using the Data
Social Impact of Dataset
Promoting the development of AI in the music industry
Discussion of Biases
Most are Chinese songs
Other Known Limitations
Samples are not balanced enough
Additional Information
Dataset Curators
Zijin Li
Evaluation
Citation Information
@article{2019Data,
title={Data Augmentation based Convolutional Neural Network for Auscultation},
author={Yiliang Jiang and Xulong Zhang and Jin Deng and Wenqiang Zhang and Wei Li},
journal={Journal of Fudan University (Natural Science)},
year={2019},
}
Contributions
Provide a dataset for music timbre and range