Microphone Array Position Self-Calibration from Reverberant Speech Input

Abstract

In this paper we propose an approach to retrieve the geome-try of an acoustic sensor network consisting of spatially dis-tributed microphone arrays from unconstrained speech input. The calibration relies on Direction of Arrival (DoA) measure-ments which do not require a clock synchronization among the sensor nodes. The calibration problem is formulated as a cost function optimization task, which minimizes the squared differences between measured and predicted observations and additionally avoids the existence of minima that correspond to mirrored versions of the actual sensor orientations. Further, outlier measurements caused by reverberation are mitigated by a Random Sample Consensus (RANSAC) approach. The experimental results show a mean positioning error of at most 25 cm even in highly reverberant environments. Index Terms — Unsupervised, geometry calibration, mi-crophone arrays, position self-calibration