用声音实时识别乒乓球击球动作与旋转，精度达毫秒级
Sound-Based Spin Estimation in Table Tennis: Dataset and Real-Time Classification Pipeline
- 结合能量峰值检测与卷积神经网络，实现音频实时处理
- 在3396个样本上实现表面接触与旋转的准确分类
- 适合机器人系统和教练工具的实时反馈场景
声音可补充视觉信息，在球类运动中提供击打动态的细微线索。在乒乓球中，球拍击球产生的短暂高频声音蕴含球拍类型、接触表面及是否施加旋转的信息。本文解决三大问题：（1）毫秒级精度的击球点精准检测；（2）击球表面（如球拍、台面、地面）分类；（3）仅靠音频实现旋转判断。为此，提出一种实时处理流程，结合基于能量的峰值检测与在新采集的3396个击球样本（覆盖10种球拍配置）上训练的卷积神经网络。系统实现了低延迟、高精度的击球检测，并可靠区分接触表面与旋转状态。该音频驱动方法为机器人系统提供了新的旋转估计路径，也适用于实时教练反馈工具。数据集与代码已公开，支持后续研究。
原文摘要 · Abstract (English)
Sound can complement vision in ball sports by providing subtle cues about contact dynamics. In table tennis, the brief, high-frequency sounds produced during racket-ball impacts carry information about the racket type, the surface contacted, and whether spin was applied. We address three key problems in this domain: (1) precise bounce detection with millisecond-level temporal accuracy, (2) classification of bounce surface (e.g., racket, table, floor), and (3) spin detection from audio alone. To this end, we propose a real-time-capable pipeline that combines energy-based peak detection with convolutional neural networks trained on a novel dataset of 3,396 bounce samples recorded across 10 racket configurations. The system achieves accurate and low-latency detection of bounces, and reliably classifies both the surface of contact and whether spin was applied. This audio-based approach opens up new possibilities for spin estimation in robotic systems and for real-time feedback in coaching tools. We publicly release both the dataset and code to support further research.
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