In this paper we present a methodology for the self-calibration of two microphone arrays based on the localization of acoustic sources from volumetric acoustic maps, one for each array. A set of correspondences are obtained moving the acoustic source at different locations in space. The proposed algorithm estimates the rigid motion that brings the coordinate system of the second microphone array to the first one through the solution of a least squares problem. The approach presented here enables the self-calibration even when the acoustic sources are in the near-field of the microphone arrays, thus extending the methodology presented by the authors in another wor

This paper proposes a method that solves the problem\ud of geometric calibration of microphone arrays. We consider a\ud distributed system, in which each array is controlled by separate\ud acquisition devices that do not share a common synchronization\ud clock. Given a set of probing sources, e.g. loudspeakers, each\ud array computes an estimate of the source locations using a\ud conventional TDOA-based algorithm. These observations are\ud fused together by the proposed method, in order to estimate\ud the position and pose of one array with respect to the other.\ud Unlike previous approaches, we explicitly consider the anisotropic\ud distribution of localization errors. As such, the proposed method\ud is able to address the problem of geometric calibration when the\ud probing sources are located both in the near- and far-field of\ud the microphone arrays. Experimental results demonstrate that\ud the improvement in terms of calibration accuracy with respect\ud to state-of-the-art algorithms can be substantial, especially in the\ud far-fiel