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

EMOPIA (pronounced ‘yee-mò-pi-uh’) dataset is a shared multi-modal (audio and MIDI) database focusing on perceived emotion in <strong>pop piano music</strong>, to facilitate research on various tasks related to music emotion. The dataset contains <strong>1,087</strong> music clips from 387 songs and <strong>clip-level</strong> emotion labels annotated by four dedicated annotators. For more detailed information about the dataset, please refer to our paper: <strong>EMOPIA: A Multi-Modal Pop Piano Dataset For Emotion Recognition and Emotion-based Music Generation</strong>. <strong>File Description</strong> <em><strong>midis/</strong></em>: 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. <em><strong>metadata/</strong></em>: metadata from YouTube. (Got when crawling) <em><strong>songs_lists/</strong></em>: YouTube URLs of songs. <em><strong>tagging_lists/</strong></em>: raw tagging result for each sample. <em><strong>label.csv</strong></em>: metadata that records filename, clip timestamps, and annotator. <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. <em><strong>scripts/prepare_split.ipynb:</strong></em> the script to create train/val/test splits and save them to csv files. <strong>Cite this dataset</strong> <pre><code>@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} }</code></pre>