基于车载分布式声学器件的手势静音控制

范梦瑶; 苏玉琪; 张扶桑; 苏林; 金蓓弘

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基于车载分布式声学器件的手势静音控制

更新时间：2026-03-03

- 基于车载分布式声学器件的手势静音控制
- Leveraging in-cabin distributed acoustic devices for gesture-based mute control
- 物联网学报 2026年
- 作者机构：
  
  1.中国科学院大学，北京 100190
  2.中国科学院软件研究所，北京 100190
  3.北京航空航天大学，北京 100191
  2.中国科学院声学研究所，北京 100190
- 作者简介：
  
  张扶桑，fusang@iscas.ac.cn
- 基金信息：
  
  北京市科技新星计划-交叉合作课题(20240484641);国家自然科学基金(62422213;62172394;62072450);北京市自然科学基金(L223034)
- DOI：
  中图分类号：
- 收稿：2025-09-15，
  
  修回：2025-11-03，
  
  录用：2026-02-09，
- 稿件说明：
移动端阅览
范梦瑶, 苏玉琪, 张扶桑, 等. 基于车载分布式声学器件的手势静音控制[J/OL]. 物联网学报, 2026.

FAN Mengyao, SU Yuqi, ZHANG Fusang, et al. Leveraging in-cabin distributed acoustic devices for gesture-based mute control[J/OL]. Chinese Journal on Internet of Things, 2026.
范梦瑶, 苏玉琪, 张扶桑, 等. 基于车载分布式声学器件的手势静音控制[J/OL]. 物联网学报, 2026. DOI：

FAN Mengyao, SU Yuqi, ZHANG Fusang, et al. Leveraging in-cabin distributed acoustic devices for gesture-based mute control[J/OL]. Chinese Journal on Internet of Things, 2026. DOI：

摘要

随着智能座舱交互技术的发展，非接触式手势识别已成为车载人机交互的重要补充。针对传统视觉与雷达方案在隐私保护、部署成本及覆盖范围方面的固有局限，提出了一种新型的手势静音控制系统。该方案复用车载分布式的多扬声器与多麦克风阵列，构建低成本的近场手势识别系统。该系统通过发送高频声学信号，并提取手部动作引起的信道冲激响应变化来识别静音手势。具体而言，为消除系统延迟，首先创建一个电回环通道以提取参考信号，该信号可用于动态计算系统延迟，实现准确的信道冲激响应的时间对齐。其次，提出了单通道多维特征判决与多通道协同决策的融合方案，通过两者的协同优化实现预设手势与环境干扰的精准区分。在多场景下开展的实验验证表明，系统静音手势识别准确率超过98%，误报率低于1.5%，响应时间小于0.05秒。

Abstract

With the rapid evolution of smart cabin interaction technology

contactless gesture recognition has become an essential component of in-cabin human-computer interaction. Traditional methods

such as vision-based and radar-based systems

often face challenges like privacy concerns

high deployment costs

and limited coverage. To overcome these limitations

we introduce a novel gesture-based mute control system that leverages the advantages of contactless interaction to enhance user experience and system efficiency. This solution repurposes existing audio hardware to construct an affordable near-field recognition system. By transmitting high-frequency acoustic signals and analyzing the changes in the channel impulse response (CIR) induced by hand movements

the system effectively recognizes mute gestures. Specifically

to eliminate system delay

we first establish an electrical loopback channel to extract a reference signal. This reference signal is then used to dynamically compute the system delay

ensuring accurate CIR time alignment. Additionally

we propose a fusion scheme that combines single-channel multi-dimensional feature judgment with a multi-channel collaborative decision-making mechanism. This integrated approach is designed to accurately distinguish predefined gestures from environmental interferences. Experimental validations across multiple scenarios show that the system achieves a mute gesture recognition accuracy exceeding 98%

a false alarm rate below 1.5%

and a response time within 0.05 seconds.

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references

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