用演奏特征预测钢琴曲情感差异，聚焦表演带来的细微情绪变化。
Learning to Predict Performance-induced Emotion Differences in Classical Piano Music
- 仅用演奏特征分离出表演影响，排除乐谱本身的干扰。
- 模型能准确捕捉不同演奏间情绪差异的方向性，但低估实际表达强度。
- 适合研究音乐表现力、情感计算或人机交互的学者参考。
音乐常被用作情感传达媒介，演奏者通过诠释塑造听众感知的情感。本研究聚焦于古典独奏钢琴音乐，利用一组6个商业录音（巴赫《平均律钢琴曲集》第一卷）进行分析，这些录音在效价（valence）与唤醒度（arousal）上进行了标注。通过仅编码表演特有特征，我们剥离了作品本身对情绪感知的影响，后者通常主导整体情绪判断。初步分析验证了这些特征在不同演奏者间存在显著差异。为此提出相对回归框架Delta-VA，用于预测相对于“平均”演奏的情感偏离，从而聚焦于特定演奏方式引起的情绪变化。除标准$R^2$评分外，还引入几何评估指标以检验演奏间情绪差值关系的保留程度。结果表明，模型在方向一致性上表现良好，但预测幅度存在压缩，即倾向于低估表演带来的表达性影响。
原文摘要 · Abstract (English)
Music is often used as a medium for communicating emotion, with performers shaping perceived affect through interpretation. This study addresses the challenge of identifying and predicting subtle changes in perceived emotion that are exclusively due to differences in performance. We focus on classical solo piano music, using a set of 6 commercial recordings of Bach's Well-Tempered Clavier Book I, annotated in terms of valence and arousal. By encoding the recordings through performance-specific features only, we isolate performance information from aspects of the composition itself, which tend to dominate the overall perceived emotional category. A preliminary analysis validates that these features vary meaningfully across performers. We then propose a relative regression framework, Delta-VA, to predict deviations in valence-arousal relative to an ``average'' performance, thereby focusing on the changes in emotion brought about by a specific way of playing a piece. In addition to the standard $R^2$ regression score, we introduce geometric evaluation metrics to assess the preservation of pairwise differences between performances. Results indicate high directional consistency with the ground truth, but also a compression in prediction magnitude, indicating that the model tends to underestimate expressive performance effects.
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