Inferring Room Semantics Using Acoustic Monitoring
This work addresses the need for environmental context in location-aware applications, but it is incremental as it builds on existing acoustic techniques with a focus on confidence updating.
The paper tackles the problem of inferring semantic labels for indoor spaces using acoustic monitoring, achieving up to 100% confidence for the true label with less than 30 audio samples in some cases.
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.