AI 中文总结
本研究针对古典钢琴音乐，提出Delta-VA相对回归框架，利用演奏特征预测演奏差异导致的情感偏差，经实验验证模型方向一致性高但存在幅度低估问题。
AI 中文摘要
音乐常被用作传递情感的媒介，演奏者通过演绎塑造感知到的情感。本研究致力于识别和预测仅由演奏差异导致的感知情感细微变化，聚焦古典独奏钢琴音乐，采用巴赫《平均律钢琴曲集》第一卷的6张商业录音，这些录音已按效价（valence）和唤醒度（arousal）标注。仅通过演奏特定特征对录音进行编码，可将演奏信息与本身往往主导整体感知情感类别的作品特征分离开来。初步分析验证了这些特征在不同演奏者间存在有意义的差异。随后，我们提出相对回归框架Delta-VA，用于预测相对于“平均”演奏的效价-唤醒度偏差，从而聚焦于特定演奏方式带来的情感变化。除标准$R^2$回归得分外，我们还引入几何评估指标，以评估演奏间成对差异的保留情况。结果显示，预测与真实值具有较高的方向一致性，但预测幅度存在压缩，表明模型倾向于低估富有表现力的演奏效果。
英文摘要
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.
CommentsAccepted by the 27th International Society for Music Information Retrieval (ISMIR)
