Speaker
Description
Acoustic localization of humans traditionally focuses on scenarios in which they emit sound. In certain settings, however, a human may remain silent while another sound source is active. Recent work demonstrated that silent humans can be localized by analyzing the subtle perturbations their bodies introduce into room impulse responses (RIRs). The existing SoundCam dataset includes RIRs estimated with a silent human present, but provides only single-snapshot RIRs per configuration. In contrast, successively measured RIR estimates enable the analysis of subtle temporal variations caused by involuntary human micromotion, e.g., breathing, which can lead to more robust localization performance than single-snapshot human‑present vs. human-absent comparisons.To advance research in this field, we introduce SuRIR, the first dataset that provides four successive RIR estimates for each configuration, measured a few seconds apart. SuRIR comprises 14,400 RIR estimates, acquired at 48 kHz using exponential sine sweeps. Measurements were conducted using six microphones and two loudspeakers across two rooms, with three participants performing five distinct movement patterns. To provide a static reference condition, SuRIR additionally includes 960 RIRs collected using a mannequin. Besides silent human localization, SuRIR provides a controlled testbed for evaluating RIR-based algorithms under subtle, real-world environmental variations.