Datasets:
license: mit
task_categories:
- audio-classification
language:
- zh
- en
tags:
- music
- art
pretty_name: Music Genre Database
size_categories:
- 10K<n<100K
viewer: false
Dataset Card for Music Genre Dataset
Dataset Description
- Homepage: https://ccmusic-database.github.io
- Repository: https://huggingface.co/datasets/ccmusic-database/music_genre
- Paper: https://doi.org/10.5281/zenodo.5676893
- Leaderboard: https://ccmusic-database.github.io/team.html
- Point of Contact: N/A
Dataset Summary
This database contains about 1700 musical pieces (.mp3 format) with lengths of 270-300s that are divided into 17 genres in total.
Supported Tasks and Leaderboards
Audio classification
Languages
Multilingual
Usage
When doing classification task, only one colum of fst_level_label, sec_level_label and thr_level_label can be used, not for mixing.
from datasets import load_dataset
dataset = load_dataset("ccmusic-database/music_genre", split="test")
for item in dataset:
print(item)
Dataset Structure
| mel | cqt | chroma | fst_level_label | sec_level_label | thr_level_label |
|---|---|---|---|---|---|
| jpg | jpg | jpg | 2-class | 9-class | 12-class |
Data Instances
.zip(.jpg)
Data Fields
1_Classic
3_Symphony
4_Opera
5_Solo
6_Chamber
2_Non_classic
7_Pop
12_Pop_vocal_ballad
13_Adult_contemporary
14_Teen_pop
8_Dance_and_house
15_Contemporary_dance_pop
16_Dance_pop
9_Indie
17_Classic_indie_pop
18_Chamber_cabaret_and_art_pop
10_Soul_or_r_and_b
11_Rock
19_Adult_alternative_rock
20_Uplifting_anthemic_rock
21_Soft_rock
22_Acoustic_pop
0_None
Data Splits
Train(80%), valid(10%), test(10%)
Dataset Creation
Curation Rationale
Promoting the development of AI in the music industry
Source Data
Initial Data Collection and Normalization
Zhaorui Liu, Monan Zhou
Who are the source language producers?
Composers of the songs in dataset
Annotations
Annotation process
Students collected about 1700 musical pieces (.mp3 format) with lengths of 270-300s divided into 17 genres in total.
Who are the annotators?
Students from CCMUSIC
Personal and Sensitive Information
Due to copyright issues with the original music, only mel spectrograms 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 English songs
Other Known Limitations
Samples are not balanced enough
Additional Information
Dataset Curators
Zijin Li
Evaluation
Coming soon...
Licensing Information
MIT License
Copyright (c) CCMUSIC
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Citation Information
@dataset{zhaorui_liu_2021_5676893,
author = {Zhaorui Liu, Monan Zhou, Shenyang Xu, Zhaowen Wang, Wei Li and Zijin Li},
title = {CCMUSIC DATABASE: A Music Data Sharing Platform for Computational Musicology Research},
month = {nov},
year = {2021},
publisher = {Zenodo},
version = {1.1},
doi = {10.5281/zenodo.5676893},
url = {https://doi.org/10.5281/zenodo.5676893}
}
Contributions
Provide a dataset for music genre classification