  Music arrangement generation is a subtask of automatic music generation,
which involves reconstructing and re-conceptualizing a piece with new
compositional techniques. Such a generation process inevitably requires
reference from the original melody, chord progression, or other structural
information. Despite some promising models for arrangement, they lack more
refined data to achieve better evaluations and more practical results. In this
paper, we propose POP909, a dataset which contains multiple versions of the
piano arrangements of 909 popular songs created by professional musicians. The
main body of the dataset contains the vocal melody, the lead instrument melody,
and the piano accompaniment for each song in MIDI format, which are aligned to
the original audio files. Furthermore, we provide the annotations of tempo,
beat, key, and chords, where the tempo curves are hand-labeled and others are
done by MIR algorithms. Finally, we conduct several baseline experiments with
this dataset using standard deep music generation algorithms.
