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---
license: mit
task_categories:
- audio-classification
language:
- zh
- en
tags:
- music
- art
pretty_name: Music Genre Dataset
size_categories:
- 10K<n<100K
viewer: false
---
# Dataset Card for Music Genre
## 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
```bash
GIT_LFS_SKIP_SMUDGE=1 git clone git@hf.co:datasets/ccmusic-database/music_genre
```
When doing classification task, only one colum of fst_level_label, sec_level_label and thr_level_label can be used, not for mixing.
```python
from datasets import load_dataset
dataset = load_dataset("ccmusic-database/music_genre", split="test")
for item in dataset:
print(item)
```
## Dataset Structure
<style>
#genres td {
vertical-align: middle !important;
text-align: center;
}
#genres th {
text-align: center;
}
</style>
<table id="genres">
<tr>
<td>mel(.jpg, 11.4s)</td>
<td>cqt(.jpg, 11.4s)</td>
<td>chroma(.jpg, 11.4s)</td>
<td>fst_level_label(2-class)</td>
<td>sec_level_label(9-class)</td>
<td>thr_level_label(16-class)</td>
</tr>
<tr>
<td><img src="https://cdn-uploads.huggingface.co/production/uploads/655e0a5b8c2d4379a71882a9/PqdpQP__ls-xo6lz93Q4y.jpeg"></td>
<td><img src="https://cdn-uploads.huggingface.co/production/uploads/655e0a5b8c2d4379a71882a9/EZfYLng40hh_FUudB9vvx.jpeg"></td>
<td><img src="https://cdn-uploads.huggingface.co/production/uploads/655e0a5b8c2d4379a71882a9/zviZ-rEKAvBCVFvKFml4R.jpeg"></td>
<td>1_Classic / 2_Non_classic</td>
<td>3_Symphony / 4_Opera / 5_Solo / 6_Chamber / 7_Pop / 8_Dance_and_house / 9_Indie / 10_Soul_or_r_and_b / 11_Rock</td>
<td>3_Symphony / 4_Opera / 5_Solo / 6_Chamber / 12_Pop_vocal_ballad / 13_Adult_contemporary / 14_Teen_pop / 15_Contemporary_dance_pop / 16_Dance_pop / 17_Classic_indie_pop / 18_Chamber_cabaret_and_art_pop / 10_Soul_or_r_and_b / 19_Adult_alternative_rock / 20_Uplifting_anthemic_rock / 21_Soft_rock / 22_Acoustic_pop</td>
</tr>
<tr>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
</tr>
</table>
### Data Instances
.zip(.jpg, .wav)
### 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
```
### Data Splits
| total | 36375 |
| :-------------: | :---: |
| train(80%) | 29100 |
| validation(10%) | 3637 |
| test(10%) | 3638 |
## 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 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
<https://huggingface.co/ccmusic-database/music_genre/tree/main>
### 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, Yuan Wang, 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 |