Pianist Transformer: Towards Expressive Piano Performance Rendering via Scalable Self-Supervised Pre-Training
arXiv:2512.02652v2 Announce Type: replace-cross Abstract: Existing methods for expressive music performance rendering, a conditional generation task that aims to generate a human-like performance from a symbolic score, rely on supervised learning over small labeled datasets, which limits scaling of both data volume and model size, despite the availability of vast unlabeled music, as in vision and language. To address this gap, we introduce Pianist Transformer, with three key contributions: 1) introducing large-scale self-supervised learning into expressive piano performance rendering through a
The development of expressive music generation is progressing rapidly due to advancements in AI and increased computational power, allowing for more nuanced artistic applications.
This development pushes the boundaries of AI in creative fields, potentially reshaping how music is composed, performed, and consumed, and creating new avenues for digital artistry.
The ability of AI to generate highly expressive piano performances from symbolic scores, minimizing human intervention, changes the landscape of music production and education.
- · AI music generation platforms
- · Music education technology
- · Digital content creators
- · Entertainment industry
- · Traditional music performance automation companies
- · Entry-level session musicians
- · Synthesizer manufacturers (for certain use cases)
AI models will increasingly handle complex artistic tasks previously requiring human nuance and skill.
This could lead to a proliferation of AI-generated expressive musical works, challenging traditional notions of musical authorship and performance.
The democratization of expressive music creation could foster new forms of interactive and personalized musical experiences through AI-driven interfaces.
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Read at arXiv cs.AI