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Metadata

Name
EMOPIA: A Multi-Modal Pop Piano Dataset For Emotion Recognition and Emotion-based Music Generation
Repository
ZENODO
Identifier
doi:10.5281/zenodo.5257995
Description
EMOPIA (pronounced &lsquo;yee-m&ograve;-pi-uh&rsquo;) dataset is a shared multi-modal (audio and MIDI) database focusing on perceived emotion in&nbsp;pop piano music, to facilitate research on various tasks related to music emotion. The dataset contains&nbsp;1,087&nbsp;music clips from 387 songs and&nbsp;clip-level&nbsp;emotion labels annotated by four dedicated annotators.&nbsp;

For more detailed information about the dataset, please refer to our paper:&nbsp;EMOPIA: A Multi-Modal Pop Piano Dataset For Emotion Recognition and Emotion-based Music Generation.&nbsp;

File Description


midis/:&nbsp;midi clips transcribed using GiantMIDI.


Filename `Q1_xxxxxxx_2.mp3`: Q1 means this clip belongs to Q1 on the V-A space; xxxxxxx is the song ID on YouTube, and the `2` means this clip is the 2nd clip taken from the full song.


metadata/:&nbsp;metadata from YouTube. (Got when crawling)

songs_lists/:&nbsp;YouTube URLs of songs.


tagging_lists/:&nbsp;raw tagging result for each sample.


label.csv: metadata that records filename, 4Q label, and annotator.


metadata_by_song.csv: list all the clips by the song. Can be used to create the train/val/test splits to avoid the same song appear in both train and test.


scripts/prepare_split.ipynb: the script to create train/val/test splits and save them to csv files.



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2.2 Update


Add tagging files in tagging_lists/ that are missing in the previous version.
Add timestamps.json&nbsp;for easier usage. It records all the timestamps in dict format. You can see scripts/load_timestamp.ipynb&nbsp;for the format example.
Add&nbsp;scripts/timestamp2clip.py:&nbsp;After the raw audio are crawled and put in audios/raw, you can use this script to get audio clips. The script will read timestamps.json&nbsp;and use the timestamp to extract clips. The clips will be saved to audios/seg&nbsp;folder.
remove 7 midi files that were added by mistake, and also corrected the number in metadata_by_song.csv.


&nbsp;

2.1 Update

Add one file and one folder:


key_mode_tempo.csv: key, mode, and tempo information extracted from files.
CP_events/:&nbsp; CP events used in our paper. Extracted using this script, and add the emotion event to the front.


Modify one folder:


The REMI_events/ files in version 2.0 contain&nbsp;some information that is not related to the paper, so remove it.


&nbsp;

2.0 Update

Add two new folders:


corpus/:&nbsp; processed data that following the&nbsp;preprocessing flow. (Please notice that although we have&nbsp;1078&nbsp;clips in our dataset, we lost some clips during steps&nbsp;1~4 of&nbsp;the flow, so the final number of clips in this&nbsp;corpus&nbsp;is&nbsp;1052, and that&#39;s the number we&nbsp;used for training the generative model.)
REMI_events/: REMI event for each midi file. They are generated using this script.


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Cite this dataset

@inproceedings{{EMOPIA},
author = {Hung, Hsiao-Tzu and Ching, Joann and Doh, Seungheon and Kim, Nabin and Nam, Juhan and Yang, Yi-Hsuan},
title = {{MOPIA}: A Multi-Modal Pop Piano Dataset For Emotion Recognition and Emotion-based Music Generation},
booktitle = {Proc. Int. Society for Music Information Retrieval Conf.},
year = {2021}
}
Data or Study Types
multiple
Source Organization
Unknown
Access Conditions
available
Year
2021
Access Hyperlink
https://doi.org/10.5281/zenodo.5257995

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