The prevalence of voice-related interaction and communication has raised concerns about privacy leakage and security. For example, millimeter-wave (mmWave) radio signals have been exploited as a potential attacker for acoustic eavesdropping. However, speaker variability and low-quality input pose significant challenges for the practical deployment of mmWave-based eavesdropping. In this paper, we proposeSPACE, an acoustic eavesdropping system to recover intelligible speech from low-quality mmWave signals, which can adapt to numerous different speakers and unseen ones.SPACEis a two-stage system that first reconstructs the spectrogram using a novelRadio TransUNetand then synthesizes the waveform through a neural vocoder. Specifically, to alleviate the negative effect of speaker variability, we introduce a speaker encoder to capture speaker features and a fusion network to condition the spectrogram reconstruction based on the extracted speaker characteristics. Further, to facilitate intelligible speech recovery from low-quality input, we design a Frequency Transformation Layer to exploit the correlation among all frequency harmonics and incorporate the neural vocoder to synthesize the speech waveform from the reconstructed spectrogram without using the contaminated phase. The experimental results show thatSPACEoutperforms existing mmWave-based approaches in scenarios with numerous different speakers and unseen speakers.

We introduce OctoNet, a large-scale, multi-modal, multi-view human activity dataset designed to advance human activity understanding and multi-modal learning. OctoNet comprises 12 heterogeneous modalities (including RGB, depth, thermal cameras, infrared arrays, audio, millimeter-wave radar, Wi-Fi, IMU, and more) recorded from 41 participants under multi-view sensor setups, yielding over 67.72M synchronized frames. The data encompass 62 daily activities spanning structured routines, freestyle behaviors, human-environment interaction, healthcare tasks, etc. Critically, all modalities are annotated by high-fidelity 3D pose labels captured via a professional motion-capture system, allowing precise alignment and rich supervision across sensors and views. OctoNet is one of the most comprehensive datasets of its kind, enabling a wide range of learning tasks such as human activity recognition, 3D pose estimation, multi-modal fusion, cross-modal supervision, and sensor foundation models. Extensive experiments have been conducted to demonstrate the sensing capacity using various baselines. OctoNet offers a unique and unified testbed for developing and benchmarking generalizable, robust models for human-centric perceptual AI.

Considering the microphone is easily affected by noise and soundproof materials, the radio frequency (RF) signal is a promising candidate to recover audio as it is immune to noise and can traverse many soundproof objects.In this paper, we introduce Radio2Speech, a system that uses RF signals to recover high quality speech from the loudspeaker.Radio2Speech can recover speech comparable to the quality of the microphone, advancing from recovering only single tone music or incomprehensible speech in existing approaches.We use Radio UNet to accurately recover speech in time-frequency domain from RF signals with limited frequency band.Also, we incorporate the neural vocoder to synthesize the speech waveform from the estimated time-frequency representation without using the contaminated phase.Quantitative and qualitative evaluations show that in quiet, noisy and soundproof scenarios, Radio2Speech achieves state-of-the-art performance and is on par with the microphone that works in quiet scenarios.