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While there are many music datasets with emotion labels in the literature,they cannot be used for research on symbolic-domain music analysis orgeneration, as there are usually audio files only. In this paper, we presentthe EMOPIA (pronounced `yee-m\`{o}-pi-uh') dataset, a shared multi-modal (audioand MIDI) database focusing on perceived emotion in pop piano music, tofacilitate research on various tasks related to music emotion. The datasetcontains 1,087 music clips from 387 songs and clip-level emotion labelsannotated by four dedicated annotators. Since the clips are not restricted toone clip per song, they can also be used for song-level analysis. We presentthe methodology for building the dataset, covering the song list curation, clipselection, and emotion annotation processes. Moreover, we prototype use caseson clip-level music emotion classification and emotion-based symbolic musicgeneration by training and evaluating corresponding models using the dataset.The result demonstrates the potential of EMOPIA for being used in futureexploration on piano emotion-related MIR tasks.

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