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.

翻译：语音窃听对个人隐私与信息安全构成日益严重的威胁。本文提出RadEar，一种基于射频反向散射的新型系统，旨在实现穿墙隐蔽语音窃听。RadEar包含两个核心组件：（i）部署于目标空间内部的无电池射频反向散射标签，以及（ii）置于房间外部的射频阅读器，负责信号解调、语音分离与去噪。该标签采用紧凑型双谐振器设计，通过分离激励频率与反射频率，在实现连续语音窃听的高效能频率调制的同时有效抑制自干扰。为应对弱信号接收与重叠语音的挑战，射频阅读器采用基于自监督学习的语音分离与去噪模型，通过无需真实标签的混音重构目标进行训练。我们在真实场景中对RadEar进行制作与评估，结果表明其能在实际约束条件下高保真地恢复与分离人类语音。