Event Topology-based Visual Microphone for Amplitude and Frequency Reconstruction

2025-10-19

AI Summary

Existing non-contact vibration measurement techniques struggle to simultaneously achieve high accuracy and practicality; although event cameras offer advantages in high-speed operation and low-light sensitivity, they fail to accurately and synchronously recover both vibration amplitude and frequency. This paper proposes a topology-aware visual microphone method that directly processes raw event streams—without requiring external illumination—to reconstruct vibration parameters. Innovatively integrating the Mapper algorithm with hierarchical density-based clustering, our approach uncovers the intrinsic topological structure of event data for the first time, enabling robust multi-source separation and synchronous parameter recovery. Experimental results demonstrate that our method significantly outperforms state-of-the-art approaches in both amplitude and frequency reconstruction accuracy, and successfully achieves concurrent identification and reconstruction of multiple acoustic sources from a single event stream.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

Abstract

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.

Problem

• Recovering vibration amplitude and frequency from event camera data
• Enabling passive vibration sensing without external illumination requirements
• Simultaneously reconstructing multiple sound sources from single event streams

Innovation

• Event topology-based visual microphone reconstructs vibrations
• Integrates Mapper algorithm with hierarchical density-based clustering
• Recovers amplitude and frequency from raw event streams