用事件相机无光重建振动的振幅与频率，精度显著提升。
Event Topology-based Visual Microphone for Amplitude and Frequency Reconstruction
- 结合拓扑分析与聚类算法，从原始事件流中提取振动结构。
- 在无外部光照下实现多声源同时分离，精度远超现有方法。
- 适合无需照明的动态系统监测，如工业设备或生物运动分析。
精确的振动测量对科学与工程中的动态系统分析至关重要，但非接触式方法常需在精度与实用性间权衡。事件相机具备高帧率、低光照感知能力，但现有方法难以准确恢复振动的振幅与频率。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中重建振动信号，无需外部照明。通过将拓扑数据分析中的Mapper算法与分层密度聚类相结合，框架有效捕捉事件数据的内在结构，实现振幅与频率的高保真恢复。实验表明，该方法显著优于以往方法，并能从单一事件流中同时解析多个声源，推动了被动、无光照振动感知的前沿发展。
原文摘要 · Abstract (English)
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.
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