MAJEPPA：在统一钢琴演奏空间中实现变形与评估
MAJEPPA: Morphing and Assessing in a Unified Piano Performance Space
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中文总结 AI 辅助
研究提出自监督框架MAJEPPA，构建含约4000条录音的数据集，调整MIDI自回归模型，引入EVPMR基准评估，实现统一钢琴演奏空间的表征学习与相关任务进展。
中文摘要 AI 辅助
我们提出MAJEPPA，这是一种自监督框架，用于学习覆盖完整技能范围的钢琴演奏表征，从初学者的练习录音到大师的音乐会录音。我们构建了MAJEPPA数据集，包含约4000条带标注的录音，涵盖六个技能水平和六种录制场景。我们调整了单个预训练的MIDI自回归模型，采用联合目标：下一个token预测学习在不同技能水平下基于乐谱的演奏生成，而InfoNCE和监督对比损失则将抽象的乐谱与演奏表征对齐到联合嵌入空间。该模型在统一框架内既能生成演奏也能理解演奏。通过引入EVPMR基准（涵盖质量评估、比赛排名、错误与技巧分类等一系列下游任务），我们对学习到的表征进行评估，展示了在构建适用于钢琴演奏空间的实用模型方面取得的进展。
英文摘要
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.
