Abstract
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.
Abstract (translated)
仅依赖传统的帧级指标来评估连续钢琴踏板深度估计任务是不完整的，因为这些指标忽略了诸如方向变化边界和踏板曲线轮廓等重要的音乐特征。为了提供更可解释且具有音乐意义的见解，我们提出了一种评估框架，该框架在标准帧级度量的基础上增加了动作级别评估（通过按压/保持/释放状态的片段来衡量方向和时间）以及手势级别的分析（评估每个按压-释放周期中的轮廓相似性）。我们将此框架应用于比较仅基于音频的方法与两个变体：一个结合了来自MIDI的符号信息，另一个在二进制值设置中训练，并且所有模型均采用了统一架构。实验结果表明，尽管帧级性能提升不大，但MIDI增强型模型在动作和手势级别上显著优于其他模型。这些发现证明我们的框架能够捕捉到传统度量无法察觉的重要音乐改进，从而提供了一种更实用有效的评估踏板深度估计模型的方法。
URL
https://arxiv.org/abs/2510.03750
