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Music arrangement generation is a subtask of automatic music generation,which involves reconstructing and re-conceptualizing a piece with newcompositional techniques. Such a generation process inevitably requiresreference from the original melody, chord progression, or other structuralinformation. Despite some promising models for arrangement, they lack morerefined data to achieve better evaluations and more practical results. In thispaper, we propose POP909, a dataset which contains multiple versions of thepiano arrangements of 909 popular songs created by professional musicians. Themain 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 tothe original audio files. Furthermore, we provide the annotations of tempo,beat, key, and chords, where the tempo curves are hand-labeled and others aredone by MIR algorithms. Finally, we conduct several baseline experiments withthis dataset using standard deep music generation algorithms.

POP909: A Pop-song Dataset for Music Arrangement Generation