The ACCompanion: Combining Reactivity, Robustness, and Musical Expressivity in an Automatic Piano Accompanist
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Abstract (arXiv)
This paper introduces the ACCompanion, an expressive accompaniment system. Similarly to a musician who accompanies a soloist playing a given musical piece, our system can produce a human-like rendition of the accompaniment part that follows the soloist's choices in terms of tempo, dynamics, and articulation. The ACCompanion works in the symbolic domain, i.e., it needs a musical instrument capable of producing and playing MIDI data, with explicitly encoded onset, offset, and pitch for each played note. We describe the components that go into such a system, from real-time score following and prediction to expressive performance generation and online adaptation to the expressive choices of the human player. Based on our experience with repeated live demonstrations in front of various audiences, we offer an analysis of the challenges of combining these components into a system that is highly reactive and precise, while still a reliable musical partner, robust to possible performance errors and responsive to expressive variations.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Apr 2023.
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Citing papers per year: 2023: 1; 2024: 2; 2025: 1.
4 papers.
- 2024DExter: Learning and Controlling Performance Expression with Diffusion Modelscited by 11
- 2023Online Symbolic Music Alignment with Offline Reinforcement Learningcited by 5
- 2024TheGlueNote: Learned Representations for Robust and Flexible Note Alignmentcited by 1
- 2025Disentangling Score Content and Performance Style for Joint Piano Rendering and Transcriptioncited by 0
- 2025Disentangling Score Content and Performance Style for Joint Piano Rendering and Transcription28 Sep 2025
- 2024TheGlueNote: Learned Representations for Robust and Flexible Note Alignment8 Aug 2024
- 2024DExter: Learning and Controlling Performance Expression with Diffusion Models21 Jun 2024
- 2023Online Symbolic Music Alignment with Offline Reinforcement Learning31 Dec 2023