  Voice interfaces has become an integral part of our lives, with the
proliferation of smart devices. Today, IoT devices mainly rely on microphones
to sense sound. Microphones, however, have fundamental limitations, such as
weak source separation, limited range in the presence of acoustic insulation,
and being prone to multiple side-channel attacks. In this paper, we propose
RadioMic, a radio-based sound sensing system to mitigate these issues and
enrich sound applications. RadioMic constructs sound based on tiny vibrations
on active sources (e.g., a speaker or human throat) or object surfaces (e.g.,
paper bag), and can work through walls, even a soundproof one. To convert the
extremely weak sound vibration in the radio signals into sound signals,
RadioMic introduces radio acoustics, and presents training-free approaches for
robust sound detection and high-fidelity sound recovery. It then exploits a
neural network to further enhance the recovered sound by expanding the
recoverable frequencies and reducing the noises. RadioMic translates massive
online audios to synthesized data to train the network, and thus minimizes the
need of RF data. We thoroughly evaluate RadioMic under different scenarios
using a commodity mmWave radar. The results show RadioMic outperforms the
state-of-the-art systems significantly. We believe RadioMic provides new
horizons for sound sensing and inspires attractive sensing capabilities of
mmWave sensing devices
