Datasets:

ccmusic-database
/

music_genre

	---
	license: cc-by-nc-nd-4.0
	task_categories:
	- audio-classification
	- image-classification
	language:
	- zh
	- en
	tags:
	- music
	- art
	pretty_name: Music Genre Dataset
	size_categories:
	- 10K<n<100K
	dataset_info:
	- config_name: eval
	features:
	- name: mel
	dtype: image
	- name: cqt
	dtype: image
	- name: chroma
	dtype: image
	- name: fst_level_label
	dtype:
	class_label:
	names:
	'0': Classic
	'1': Non_classic
	- name: sec_level_label
	dtype:
	class_label:
	names:
	'0': Symphony
	'1': Opera
	'2': Solo
	'3': Chamber
	'4': Pop
	'5': Dance_and_house
	'6': Indie
	'7': Soul_or_RnB
	'8': Rock
	- name: thr_level_label
	dtype:
	class_label:
	names:
	'0': Symphony
	'1': Opera
	'2': Solo
	'3': Chamber
	'4': Pop_vocal_ballad
	'5': Adult_contemporary
	'6': Teen_pop
	'7': Contemporary_dance_pop
	'8': Dance_pop
	'9': Classic_indie_pop
	'10': Chamber_cabaret_and_art_pop
	'11': Soul_or_RnB
	'12': Adult_alternative_rock
	'13': Uplifting_anthemic_rock
	'14': Soft_rock
	'15': Acoustic_pop
	splits:
	- name: train
	num_bytes: 19661943
	num_examples: 29100
	- name: validation
	num_bytes: 2453757
	num_examples: 3637
	- name: test
	num_bytes: 2456508
	num_examples: 3638
	download_size: 4436653005
	dataset_size: 24572208
	configs:
	- config_name: eval
	data_files:
	- split: train
	path: eval/train/data-*.arrow
	- split: validation
	path: eval/validation/data-*.arrow
	- split: test
	path: eval/test/data-*.arrow
	---

	# Dataset Card for Music Genre
	The Default 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 Structure
	<style>
	.genres td {
	vertical-align: middle !important;
	text-align: center;
	}
	.genres th {
	text-align: center;
	}
	</style>

	### Eval Subset
	<table class="genres">
	<tr>
	<th>mel</th>
	<th>cqt</th>
	<th>chroma</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>.jpg, 11.4s, 48000Hz</td>
	<td>.jpg, 11.4s, 48000Hz</td>
	<td>.jpg, 11.4s, 48000Hz</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>
	</table>

	### Data Instances
	.zip(.jpg)

	### Data Fields
	```txt
	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_RnB

	11_Rock
	19_Adult_alternative_rock
	20_Uplifting_anthemic_rock
	21_Soft_rock
	22_Acoustic_pop
	```

	![](https://www.modelscope.cn/datasets/ccmusic-database/music_genre/resolve/master/data/genre.png)

	### Data Splits
	\| Splits \| Eval \|
	\| :-------------: \| :---: \|
	\| train(80%) \| 29100 \|
	\| validation(10%) \| 3637 \|
	\| test(10%) \| 3638 \|
	\| total \| 36375 \|

	## Dataset Description
	### 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
	### Eval Subset
	```python
	from datasets import load_dataset

	ds = 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)
	```

	## Maintenance
	```bash
	GIT_LFS_SKIP_SMUDGE=1 git clone [email protected]:datasets/ccmusic-database/music_genre
	cd music_genre
	```

	## Mirror
	<https://www.modelscope.cn/datasets/ccmusic-database/music_genre>

	## 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>

	### Citation Information
	```bibtex
	@dataset{zhaorui_liu_2021_5676893,
	author = {Zhaorui Liu and Zijin Li},
	title = {Music Data Sharing Platform for Computational Musicology Research (CCMUSIC DATASET)},
	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