标题： 使用音乐信息度量评估高分辨率钢琴延音踏板深度估计 显示英文标题

标题： Evaluating High-Resolution Piano Sustain Pedal Depth Estimation with Musically Informed Metrics

摘要： 连续钢琴踏板深度估计任务的评估在仅依赖传统帧级指标时仍然不完整，这些指标忽略了音乐中重要的特征，如方向变化边界和踏板曲线轮廓。 为了提供更具可解释性和音乐意义的见解，我们提出了一种评估框架，该框架在标准帧级指标的基础上，增加了动作级评估，通过按压/保持/释放状态的片段来测量方向和时间，并进行了手势级分析，以评估每个按压-释放周期的轮廓相似性。 我们将此框架应用于比较一个仅音频基线模型与两个变体：一个结合了来自MIDI的符号信息，另一个在二值化设置中进行训练，所有模型都在统一架构内进行训练。 结果表明，尽管帧级提升较小，但MIDI启发的模型在动作和手势层面显著优于其他模型。 这些发现表明，我们的框架能够捕捉到传统指标无法察觉的音乐相关改进，为评估踏板深度估计模型提供了一种更实用和有效的方法。 显示英文摘要

摘要： 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.