以鼓之形听其室：基于模态引导的多点表面振动声音恢复
Hearing the Room Through the Shape of the Drum: Modal-Guided Sound Recovery from Multi-Point Surface Vibrations
把「视觉麦克风」从薄膜/扬声器等良响应物体推广到响应差或强共振的固体：用散斑成像同时采集多点多轴表面振动，再借物体振动模态建立物理正演模型并反演共振传递函数，为基于视觉的非接触声学传感提供了一条可复用的多测点融合反演思路。
原文依据 we derive a novel physics-guided vibration formation model that relates the scene sound source to the captured multi-point multi-axis vibrations via the object's vibrational modes
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论光学振动传感使得可以直接从邻近物体的表面振动中恢复场景声音，从而把日常物体变成「视觉麦克风」。然而，此前的大多数方法都聚焦于采集那些振动响应极为有利的特定物体的振动，这类物体包括表面振动由物体自身产生的物体（例如扬声器振膜或吉他琴身），以及由对声音高度敏感的薄膜构成的物体（例如薯片袋或植物叶片）。本文针对一类更具挑战性的固体物体开展声音恢复，这类物体的振动响应较差或具有强共振特性。我们利用基于散斑的振动测量成像系统，同时采集物体上多个表面点的振动。随后，我们推导出一个新的物理引导的振动形成模型，该模型通过物体的振动模态，将场景声源与所采集的多点、多轴振动联系起来。该模型随后被用于反演振动物体的共振传递函数，融合多路振动信号以估计场景的原始声源。我们通过对多种日常物体进行声音恢复来评估所提方法，结果表明，在传统单点散斑振动测量表现不佳的挑战性场景中，本方法显著优于后者。
英文原文摘要 1405 字符
Optical vibration sensing enables recovering the scene sound directly from the surface vibration of nearby objects, turning everyday objects into "visual microphones". However, most prior methods had focused on capturing the vibrations of specific objects with highly favorable vibration responses. These include objects where the surface vibrations are generated by the object itself (e.g., speaker membrane or guitar body) or objects consisting of a thin membrane which is highly reactive to sound (e.g., a chip bag or the leaf of a plant).In this paper, we tackle sound recovery for a more challenging class of solid objects whose vibration responses are poor or highly resonant. We simultaneously capture vibrations for multiple surface points on the object using a speckle-based vibrometry imaging system. Then, we derive a novel physics-guided vibration formation model that relates the scene sound source to the captured multi-point multi-axis vibrations via the object's vibrational modes. The model is then used to reverse the resonant transfer function of the vibrating object, fusing the plurality of vibration signals to estimate the original sound source of the scene. We evaluate our approach by recovering sound from a variety of everyday objects, demonstrating that it significantly outperforms traditional single-point speckle vibrometry in challenging scenarios where it performs poorly.