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An ability to detect, classify, and locate complex acoustic events can be a powerful tool to help smart systems build context-awareness, e.g., to make rich inferences about human behaviors in physical spaces. Conventional methods to measure acoustic signals employ microphones as sensors. As signals from multiple acoustic sources are blended during propagation to a sensor, such methods impose a dual challenge of separating the signal for an acoustic event from background noise and from other acoustic events of interest. Recent research has proposed using radio-frequency (RF) signals, e.g., Wi-Fi and millimeter-wave (mmWave), to sense sound directly from source vibrations. Whereas these works allow separating an acoustic event from background noise, they cannot monitor multiple sound sources simultaneously. In this paper, we present UWHear, a system that simultaneously recovers and separates …