README.md

---
license: mit
task_categories:
- audio-classification
- image-classification
language:
- zh
- en
tags:
- music
- art
pretty_name: Music Genre Dataset
size_categories:
- 10K<n<100K
viewer: false
---

# Dataset Card for Music Genre
The raw dataset comprises approximately 1,700 musical pieces in .mp3 format, sourced from the NetEase music. The lengths of these pieces range from 270 to 300 seconds. All are sampled at the rate of 22,050 Hz. As the website providing the audio music includes style labels for the downloaded music, there are no specific annotators involved. Validation is achieved concurrently with the downloading process. They are categorized into a total of 16 genres.

## 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://www.modelscope.cn/datasets/ccmusic/music_genre>
- **Point of Contact:** <https://huggingface.co/ccmusic-database/music_genre>

### 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

## Maintenance
```bash
GIT_LFS_SKIP_SMUDGE=1 git clone git@hf.co:datasets/ccmusic-database/music_genre
cd music_genre
```

## Usage
### Eval Subset
```python
from datasets import load_dataset

dataset = load_dataset("ccmusic-database/music_genre", name="eval")
for item in ds["train"]:
    print(item)

for item in ds["validation"]:
    print(item)

for item in ds["test"]:
    print(item)
```

### Raw Subset
```python
from datasets import load_dataset

dataset = load_dataset("ccmusic-database/music_genre", name="default")
for item in ds["train"]:
    print(item)

for item in ds["validation"]:
    print(item)

for item in ds["test"]:
    print(item)
``` 

## Dataset Structure
### Eval Subset
<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, 48000Hz)</td>
        <td>cqt(.jpg, 11.4s, 48000Hz)</td>
        <td>chroma(.jpg, 11.4s, 48000Hz)</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="./data/PqdpQP__ls-xo6lz93Q4y.jpeg"></td>
        <td><img src="./data/EZfYLng40hh_FUudB9vvx.jpeg"></td>
        <td><img src="./data/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>

### Raw Subset
<table>
    <tr>
        <th>audio(.wav, 22050Hz)</th>
        <th>mel(spectrogram, .jpg, 22050Hz)</th>
        <th>fst_level_label(2-class)</th>
        <th>sec_level_label(9-class)</th>
        <th>thr_level_label(16-class)</th>
    </tr>
    <tr>
        <td><audio controls src="./data/8bb58041d6b9d35db688bcedfde0fe39.mp3"></audio></td>
        <td><img src="./data/8bb58041d6b9d35db688bcedfde0fe39.jpg"></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>
    </tr>
</table>

### 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
```

### Data Splits
|      Split      | Eval  |  Raw  |
| :-------------: | :---: | :---: |
|      total      | 36375 | 1713  |
|   train(80%)    | 29100 | 1370  |
| validation(10%) | 3637  |  171  |
|    test(10%)    | 3638  |  172  |

## 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 the 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
```bibtex
@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 music genre classification