用无线电反射技术隐蔽窃听墙内语音并分离声音
RadEar: A Self-Supervised RF Backscatter System for Voice Eavesdropping and Separation
- 通过电池供电的反射标签和外部读取器实现隐蔽语音捕获
- 在真实场景中高保真还原并分离墙内重叠人声
- 无需标注数据，自监督学习提升语音分离与降噪能力
窃听语音对话正对个人隐私与信息安全构成日益严重的威胁。本文提出RadEar，一种基于射频（RF）背散射的新型系统，可实现穿透墙壁的隐蔽语音窃听。该系统包含两个核心组件：(i) 隐蔽部署于目标空间内的无电池射频背散射标签，(ii) 位于房间外的射频读取器，负责信号解调、语音分离与降噪。标签采用紧凑型双谐振器设计，通过分离激励与反射频率，实现低功耗频率调制，同时有效抑制自干扰。为应对信号微弱和语音重叠的挑战，读取器采用自监督学习模型进行语音分离与降噪，训练基于混音重构目标，无需真实标签。我们在真实环境中制作并评估了RadEar系统，证明其在实际约束下仍能以高保真度恢复并分离人类语音。
原文摘要 · Abstract (English)
Eavesdropping on voice conversations presents a growing threat to personal privacy and information security. In this paper, we present RadEar, a novel RF backscatter-based system designed to enable covert voice eavesdropping through walls. RadEar consists of two key components: (i) a batteryless RF backscatter tag covertly deployed inside the target space, and (ii) an RF reader located outside the room that performs signal demodulation, voice separation, and denoising. The tag features a compact, dual-resonator design that achieves energy-efficient frequency modulation for continuous voice eavesdropping while mitigating self-interference by separating excitation and reflection frequencies. To overcome the challenges of weak signal reception and overlapping speech, the RF reader employs self-supervised learning models for voice separation and denoising, trained using a remix-based objective without requiring ground-truth labels. We fabricate and evaluate RadEar in real-world scenarios, demonstrating its ability to recover and separate human speech with high fidelity under practical constraints.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。