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