A Framework for Infrastructure-Free Indoor Localization Based on Pervasive Sound Analysis

Even as modern indoor positioning systems become more precise and computationally lightweight, most rely on specific infrastructure to be installed, leading to increased setup and maintenance costs. As such, multiple infrastructure-free solutions were devised relying on signals such as magnetic field, ambient light, and movement. In this paper, we propose a framework for determining the user's location through the sound recorded by the user's device. With this goal, we present two algorithms: SoundSignature and SoundSimilarity. With SoundSignature, we extract acoustic fingerprints from the recorded audio and employ them in a support vector machine classifier. With SoundSimilarity, where we employ a novel audio similarity measure to detect if users are in the same location as other users or microphone equipped devices. Both of these algorithms require no infrastructure and are computationally lightweight, thus allowing their use either in conjunction with other infrastructure-free technologies or standalone. The training of these algorithms requires nothing more than a smartphone or a similar device under normal usage conditions, eliminating the need of any dedicated equipment.