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arXiv · 2608.11026

MAJEPPA: Morphing and Assessing in a Unified Piano Performance Space

Jinwen Zhou·Huan Zhang·Weixi Zhai·Jinhua Liang·Aidan O. T. Hogg·Simon Dixon
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Abstract

We present MAJEPPA, a self-supervised framework to learn piano performance representations that span the full skill spectrum, from beginner practice sessions to virtuoso concert recordings. We curate the MAJEPPA dataset, comprising ~4,000 annotated recordings across six expertise levels and six recording contexts. We adapt a single pre-trained MIDI autoregressive model with a joint objective: next-token prediction learns score-conditioned performance generation at various skill levels, while InfoNCE and supervised contrastive losses align abstract score and performance representations in a joint embedding space. The proposed model both generates and understands performances in a unified framework. By introducing the EVPMR benchmark, a suite of downstream tasks spanning quality assessment, competition ranking, mistake and technique classification, we evaluate the learnt representations, demonstrating progress towards a real-world model for the piano performance space.

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Jinwen Zhou, Huan Zhang, Weixi Zhai, Jinhua Liang, Aidan O. T. Hogg, Simon Dixon. 2026-08-11. MAJEPPA: Morphing and Assessing in a Unified Piano Performance Space. https://arxiv.org/abs/2608.11026

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