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.

翻译：被动式人体速度估计在声学传感中扮演着关键角色。然而，尽管已有广泛研究，现有系统仍存在诸多局限：首先，信道测量速率不足以估计高速运动目标。其次，以往的声学速度估计方法利用运动目标产生的多普勒频移（DFS），并依赖于麦克风阵列，使其仅能感知有限距离内的径向速度。为克服这些问题，我们提出了ASE——一种基于单个商用麦克风的精确且鲁棒的声学速度估计系统。我们提出了一种新颖的正交时延复用（OTDM）方案，用于实现先前不可行的高速率声学信道估计，从而使得高速速度估计成为可能。随后，我们从声扩散场的独特视角对声音传播进行建模，并通过声学空间分布推断速度，这是一种完全不同于以往基于DFS方法的全新速度估计思路。我们进一步开发了用于运动检测和信号增强的新技术，以构建一个鲁棒且实用的系统。我们通过大量真实世界实验对ASE进行了实现与评估。结果表明，ASE能够可靠地追踪步行速度，且不受目标位置和方向的影响，其平均误差为0.13 m/s，较DFS方法降低了2.5倍，并在大范围覆盖（例如在4m×4m房间内自由行走）下实现了97.4%的检测率。我们相信，ASE将声学速度估计推向了超越传统基于DFS范式的新阶段，并为声学传感领域的研究带来了新的启发。代码发布于https://github.com/aiot-lab/ASE。