POP909: A Pop-song Dataset for Music Arrangement Generation

TL;DR

This paper introduces POP909, a comprehensive dataset of professional piano arrangements for 909 popular songs, including aligned MIDI data and annotations, to advance music arrangement generation research.

Contribution

The paper presents POP909, a new dataset with detailed annotations and multiple arrangement versions, facilitating improved evaluation and development of music arrangement models.

Findings

Baseline experiments demonstrate the dataset's utility for deep music generation.

Annotations and aligned MIDI data enable more refined arrangement modeling.

The dataset supports diverse research in automatic music arrangement.

Abstract

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.