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EMOPIA: A Multi-Modal Pop Piano Dataset For Emotion Recognition and Emotion-based Music Generation

Hung, Hsiao-Tzu; Ching, Joann; Doh, Seungheon; Kim, Nabin; Nam, Juhan; Yang, Yi-Hsuan


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    "description": "<p>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;<strong>pop piano music</strong>, to facilitate research on various tasks related to music emotion. The dataset contains&nbsp;<strong>1,087</strong>&nbsp;music clips from 387 songs and&nbsp;<strong>clip-level</strong>&nbsp;emotion labels annotated by four dedicated annotators.&nbsp;</p>\n\n<p>For more detailed information about the dataset, please refer to our paper:&nbsp;<a href=\"https://arxiv.org/abs/2108.01374\"><strong>EMOPIA: A Multi-Modal Pop Piano Dataset For Emotion Recognition and Emotion-based Music Generation</strong></a>.&nbsp;</p>\n\n<p><strong>File Description</strong></p>\n\n<ul>\n\t<li><em><strong>midis/</strong></em>:&nbsp;midi clips transcribed using GiantMIDI.\n\n\t<ul>\n\t\t<li>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.</li>\n\t</ul>\n\t</li>\n\t<li><em><strong>metadata/</strong></em>:&nbsp;metadata from YouTube. (Got when crawling)</li>\n\t<li>\n\t<p><em><strong>songs_lists/</strong></em>:&nbsp;YouTube URLs of songs.</p>\n\t</li>\n\t<li>\n\t<p><em><strong>tagging_lists/</strong></em>:&nbsp;raw tagging result for each sample.</p>\n\t</li>\n\t<li>\n\t<p><em><strong>label.csv</strong></em>: metadata that records filename, clip timestamps, and annotator.</p>\n\t</li>\n\t<li>\n\t<p><em><strong>metadata_by_song.csv</strong></em>: 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.</p>\n\t</li>\n\t<li>\n\t<p><em><strong>scripts/prepare_split.ipynb:</strong></em> the script to create train/val/test splits and save them to csv files.</p>\n\t</li>\n</ul>\n\n<p>&nbsp;</p>\n\n<p><strong>Cite this dataset</strong></p>\n\n<pre><code>@inproceedings{{EMOPIA},\n         author = {Hung, Hsiao-Tzu and Ching, Joann and Doh, Seungheon and Kim, Nabin and Nam, Juhan and Yang, Yi-Hsuan},\n         title = {{MOPIA}: A Multi-Modal Pop Piano Dataset For Emotion Recognition and Emotion-based Music Generation},\n         booktitle = {Proc. Int. Society for Music Information Retrieval Conf.},\n         year = {2021}\n}</code></pre>", 
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    "title": "EMOPIA: A Multi-Modal Pop Piano Dataset For Emotion Recognition and Emotion-based Music Generation", 
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    "keywords": [
      "piano", 
      "emotion", 
      "music", 
      "midi"
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    "publication_date": "2021-07-18", 
    "creators": [
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        "affiliation": "Academia Sinica", 
        "name": "Hung, Hsiao-Tzu"
      }, 
      {
        "affiliation": "Academia Sinica", 
        "name": "Ching, Joann"
      }, 
      {
        "affiliation": "KAIST", 
        "name": "Doh, Seungheon"
      }, 
      {
        "affiliation": "Georgia Institute of Technology", 
        "name": "Kim, Nabin"
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        "orcid": "0000-0003-2664-2119", 
        "affiliation": "KAIST", 
        "name": "Nam, Juhan"
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        "orcid": "0000-0002-2724-6161", 
        "affiliation": "Academia Sinica", 
        "name": "Yang, Yi-Hsuan"
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