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标题: "MAJEPPA: Morphing and Assessing in a Unified Piano Performance Space"
原文链接: "https://arxiv.org/abs/2608.11026"
发布日期: "2026-08-11"
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## Electrical Engineering and Systems Science > Audio and Speech Processing

## Title:MAJEPPA: Morphing and Assessing in a Unified Piano Performance Space

Authors:[Jinwen Zhou](https://arxiv.org/search/eess?searchtype=author&query=Zhou,+J), [Huan Zhang](https://arxiv.org/search/eess?searchtype=author&query=Zhang,+H), [Weixi Zhai](https://arxiv.org/search/eess?searchtype=author&query=Zhai,+W), [Jinhua Liang](https://arxiv.org/search/eess?searchtype=author&query=Liang,+J), [Aidan O. T. Hogg](https://arxiv.org/search/eess?searchtype=author&query=Hogg,+A+O+T), [Simon Dixon](https://arxiv.org/search/eess?searchtype=author&query=Dixon,+S)

[View PDF](https://arxiv.org/pdf/2608.11026) [HTML (experimental)](https://arxiv.org/html/2608.11026v1)

> 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.

| Subjects: | Audio and Speech Processing (eess.AS); Multimedia (cs.MM) |
| --- | --- |
| Cite as: | [arXiv:2608.11026](https://arxiv.org/abs/2608.11026) \[eess.AS\] |
|  | (or [arXiv:2608.11026v1](https://arxiv.org/abs/2608.11026v1) \[eess.AS\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.11026](https://doi.org/10.48550/arXiv.2608.11026) |

## Submission history

From: Jinwen Zhou \[[view email](https://arxiv.org/show-email/8ad28bb9/2608.11026)\]  
**\[v1\]** Tue, 11 Aug 2026 15:07:09 UTC (3,372 KB)

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2608.11026) | Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))
