用音乐感知指标评估钢琴踏板深度，发现传统方法忽略关键细节。
Evaluating High-Resolution Piano Sustain Pedal Depth Estimation with Musically Informed Metrics
- 引入动作级和姿态级评估，捕捉踏板方向与曲线变化。
- 融合MIDI信息的模型在动作与姿态层面显著优于其他方法。
- 适合关注音乐表现力评估的研究者与音频处理开发者。
连续钢琴踏板深度估计的评估若仅依赖传统帧级指标，会忽略方向变化边界和踏板曲线轮廓等音乐重要特征。为此，我们提出一个评估框架，通过动作级评估（测量按压/保持/释放阶段的方向与时间）和姿态级分析（评估每个按-放周期的轮廓相似性），补充标准帧级指标。在统一架构下，对比了纯音频基线、引入MIDI符号信息的变体，以及二值化训练的模型。结果表明，尽管在帧级指标上提升有限，融合MIDI信息的模型在动作与姿态层级显著领先。这说明新框架能捕捉传统指标无法察觉的音乐相关改进，为踏板深度估计提供了更贴近实际音乐表达的评估方式。
原文摘要 · Abstract (English)
Evaluation for continuous piano pedal depth estimation tasks remains incomplete when relying only on conventional frame-level metrics, which overlook musically important features such as direction-change boundaries and pedal curve contours. To provide more interpretable and musically meaningful insights, we propose an evaluation framework that augments standard frame-level metrics with an action-level assessment measuring direction and timing using segments of press/hold/release states and a gesture-level analysis that evaluates contour similarity of each press-release cycle. We apply this framework to compare an audio-only baseline with two variants: one incorporating symbolic information from MIDI, and another trained in a binary-valued setting, all within a unified architecture. Results show that the MIDI-informed model significantly outperforms the others at action and gesture levels, despite modest frame-level gains. These findings demonstrate that our framework captures musically relevant improvements indiscernible by traditional metrics, offering a more practical and effective approach to evaluating pedal depth estimation models.
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