--- license: mit task_categories: - audio-classification - image-classification language: - zh - en tags: - music - art pretty_name: Music Genre Dataset size_categories: - 10K - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### 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
mel(.jpg, 11.4s, 48000Hz) cqt(.jpg, 11.4s, 48000Hz) chroma(.jpg, 11.4s, 48000Hz) fst_level_label(2-class) sec_level_label(9-class) thr_level_label(16-class)
1_Classic / 2_Non_classic 3_Symphony / 4_Opera / 5_Solo / 6_Chamber / 7_Pop / 8_Dance_and_house / 9_Indie / 10_Soul_or_r_and_b / 11_Rock 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
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### Raw Subset
audio(.wav, 22050Hz) mel(spectrogram, .jpg, 22050Hz) fst_level_label(2-class) sec_level_label(9-class) thr_level_label(16-class)
1_Classic / 2_Non_classic 3_Symphony / 4_Opera / 5_Solo / 6_Chamber / 7_Pop / 8_Dance_and_house / 9_Indie / 10_Soul_or_r_and_b / 11_Rock 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
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### 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 ### 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