Accurate vibration measurement is vital for analyzing dynamic systems across science and engineering, yet noncontact methods often balance precision against practicality. Event cameras offer high-speed, low-light sensing, but existing approaches fail to recover vibration amplitude and frequency with sufficient accuracy. We present an event topology-based visual microphone that reconstructs vibrations directly from raw event streams without external illumination. By integrating the Mapper algorithm from topological data analysis with hierarchical density-based clustering, our framework captures the intrinsic structure of event data to recover both amplitude and frequency with high fidelity. Experiments demonstrate substantial improvements over prior methods and enable simultaneous recovery of multiple sound sources from a single event stream, advancing the frontier of passive, illumination-free vibration sensing.

翻译：精确的振动测量对于科学与工程领域的动态系统分析至关重要，然而非接触式方法往往需要在精度与实用性之间进行权衡。事件相机具备高速、弱光感知能力，但现有方法无法以足够精度恢复振动振幅与频率。本文提出一种基于事件拓扑的视觉麦克风，可直接从原始事件流重建振动信号而无需外部照明。通过将拓扑数据分析中的Mapper算法与基于密度的层次聚类相结合，本框架捕捉事件数据的内在结构，以高保真度同时恢复振幅与频率。实验结果表明，该方法较现有方法有显著提升，并能从单一事件流中同时恢复多个声源，推动了无源、免照明振动感知技术的前沿发展。