MS
M.M. Steensma
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Detecting changes in indoor environments is essential for applications such as automated inventory monitoring.We address this problem using only active acoustic sensing: a speaker emits a known signal, distributed microphones record the sound, the Room Impulse Responses (RIRs) are obtained through deconvolution, and a neural network determines which objects have been added or removed. We address two RIR-based tasks for passive objects: object count regression, which estimates the number of silent objects present in a room from multi-channel RIRs, and multi-label binary change detection, which predicts per-object presence changes between two observations. To the best of our knowledge, neither task has been previously addressed in the acoustic domain for passive objects. Both tasks are solved using a VGGish-based architecture operating on log-Mel spectrograms. To overcome the scarcity of real-world training data, we develop a simulation pipeline using SoundSpaces 2.0and Blender to generate RIRs for arbitrary room configurations.We validate our approach through four incremental experiments.First, we reproduce a localization baseline on real RIRs from the SoundCam dataset, achieving a mean error of 15.5 cm with ten microphones at 48 kHz. Second, we quantify the sim-to-real domain gap, finding that in-domain simulated performance(18.2 cm) approaches the real baseline but cross-domain transfer fails, with errors exceeding the random-guess baseline. Third,in simulation, object count regression achieves a mean error of 0.38 boxes within the training distribution, well below the random baseline of 1.23, though extrapolation to unseen counts degrades (mean error of 0.67 boxes). Fourth, the binary change detection model correctly identifies changes for nine out of ten object locations, achieving a mean per-box accuracy of 0.935,well above the 0.5 random baseline. Direct-path occlusion reduces mean accuracy from 0.935 to 0.840, with failures concentrated at spatially clustered locations. Models trained on small change events (at most three simultaneous changes) generalize meaningfully to larger change magnitudes, retaining AUC above 0.9for seven of ten locations. These results provide initial evidence for the feasibility of acoustic change detection for multiple silent objects, while highlighting the sim-to-real gap as the primary barrier to practical deployment.
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Detecting changes in indoor environments is essential for applications such as automated inventory monitoring.We address this problem using only active acoustic sensing: a speaker emits a known signal, distributed microphones record the sound, the Room Impulse Responses (RIRs) are obtained through deconvolution, and a neural network determines which objects have been added or removed. We address two RIR-based tasks for passive objects: object count regression, which estimates the number of silent objects present in a room from multi-channel RIRs, and multi-label binary change detection, which predicts per-object presence changes between two observations. To the best of our knowledge, neither task has been previously addressed in the acoustic domain for passive objects. Both tasks are solved using a VGGish-based architecture operating on log-Mel spectrograms. To overcome the scarcity of real-world training data, we develop a simulation pipeline using SoundSpaces 2.0and Blender to generate RIRs for arbitrary room configurations.We validate our approach through four incremental experiments.First, we reproduce a localization baseline on real RIRs from the SoundCam dataset, achieving a mean error of 15.5 cm with ten microphones at 48 kHz. Second, we quantify the sim-to-real domain gap, finding that in-domain simulated performance(18.2 cm) approaches the real baseline but cross-domain transfer fails, with errors exceeding the random-guess baseline. Third,in simulation, object count regression achieves a mean error of 0.38 boxes within the training distribution, well below the random baseline of 1.23, though extrapolation to unseen counts degrades (mean error of 0.67 boxes). Fourth, the binary change detection model correctly identifies changes for nine out of ten object locations, achieving a mean per-box accuracy of 0.935,well above the 0.5 random baseline. Direct-path occlusion reduces mean accuracy from 0.935 to 0.840, with failures concentrated at spatially clustered locations. Models trained on small change events (at most three simultaneous changes) generalize meaningfully to larger change magnitudes, retaining AUC above 0.9for seven of ten locations. These results provide initial evidence for the feasibility of acoustic change detection for multiple silent objects, while highlighting the sim-to-real gap as the primary barrier to practical deployment.