ASE: Practical Acoustic Speed Estimation Beyond Doppler via Sound Diffusion Field

TL;DR

ASE introduces a novel acoustic speed estimation method using sound diffusion fields and high-rate channel estimation, enabling accurate, robust, and high-speed detection with a single microphone, surpassing traditional Doppler-based techniques.

Contribution

The paper presents ASE, a new system that estimates high human speeds acoustically using sound diffusion modeling and a novel OTDM scheme on a single microphone, overcoming previous limitations.

Findings

Achieves a mean error of 0.13 m/s in speed estimation.

Reduces error by 2.5x compared to Doppler Frequency Shift methods.

Detects walking speeds with 97.4% accuracy in real-world experiments.

Abstract

Passive human speed estimation plays a critical role in acoustic sensing. Despite extensive study, existing systems, however, suffer from various limitations: First, the channel measurement rate proves inadequate to estimate high moving speeds. Second, previous acoustic speed estimation exploits Doppler Frequency Shifts (DFS) created by moving targets and relies on microphone arrays, making them only capable of sensing the radial speed within a constrained distance. To overcome these issues, we present ASE, an accurate and robust Acoustic Speed Estimation system on a single commodity microphone. We propose a novel Orthogonal Time-Delayed Multiplexing (OTDM) scheme for acoustic channel estimation at a high rate that was previously infeasible, making it possible to estimate high speeds. We then model the sound propagation from a unique perspective of the acoustic diffusion field, and infer the speed from the acoustic spatial distribution, a completely different way of thinking about speed estimation beyond prior DFS-based approaches. We further develop novel techniques for motion detection and signal enhancement to deliver a robust and practical system. We implement and evaluate ASE through extensive real-world experiments. Our results show that ASE reliably tracks walking speed, independently of target location and direction, with a mean error of 0.13 m/s, a reduction of 2.5x from DFS, and a detection rate of 97.4% for large coverage, e.g., free walking in a 4m x 4m room. We believe ASE pushes acoustic speed estimation beyond the conventional DFS-based paradigm and inspires exciting research in acoustic sensing. Code is available at https://github.com/aiot-lab/ASE.