Probabilistic Structure from Sound

摘要

Auditory perception is one of the most important functions for robotics applications. Microphone arrays are widely used for auditory perception in which the spatial structure of microphones is usually known. In practice, microphone array calibration can be tedious and other devices or means are required. The structure from sound (SFS) approach addresses the problem of simultaneously localizing a set of microphones and a set of acoustic events that provides a great flexibility to calibrate different setups of microphone arrays. However, the existing method does not take measurement uncertainty into account and does not provide uncertainty estimates of the SFS results. In this paper, we propose a probabilistic structure from sound (PSFS) approach using the unscented transform in which the uncertainties of the PSFS results are also available. In addition, a probabilistic sound source localization approach using the PSFS results is provided to improve sound source localization accuracy. The ample results of simulation and experiments using low-cost, off-the-shelf microphones demonstrate the feasibility and performance of the proposed PSFS approach.