Inferring Room Semantics Using Acoustic Monitoring

TL;DR

This paper presents an acoustic monitoring method that infers the semantic labels of indoor spaces over time using impulse responses and ambient sounds, aiding location-aware applications.

Contribution

It introduces a novel technique combining impulse response analysis and ambient sound classification to determine room semantics dynamically.

Findings

Confidence in correct labels often exceeds 90% with fewer than 30 samples.

The method effectively distinguishes different room types based on acoustic signatures.

Performance improves with more recordings, reaching near certainty in some cases.

Abstract

Having knowledge of the environmental context of the user i.e. the knowledge of the users' indoor location and the semantics of their environment, can facilitate the development of many of location-aware applications. In this paper, we propose an acoustic monitoring technique that infers semantic knowledge about an indoor space \emph{over time,} using audio recordings from it. Our technique uses the impulse response of these spaces as well as the ambient sounds produced in them in order to determine a semantic label for them. As we process more recordings, we update our \emph{confidence} in the assigned label. We evaluate our technique on a dataset of single-speaker human speech recordings obtained in different types of rooms at three university buildings. In our evaluation, the confidence\emph{ }for the true label generally outstripped the confidence for all other labels and in some cases converged to 100\% with less than 30 samples.