A robust and passive method for geometric calibration of large arrays

TL;DR

This paper introduces a passive, noise-based method for accurately estimating the geometry of large, arbitrarily shaped microphone arrays, overcoming challenges of traditional measurement techniques.

Contribution

It develops a two-step strategy combining coherence analysis and an improved multidimensional scaling algorithm to robustly determine array geometry from ambient noise.

Findings

Successfully estimates geometry of large arrays in experiments

Handles outliers effectively in the MDS process

Demonstrates applicability to arrays with hundreds of microphones

Abstract

This paper presents a complete strategy for the geometry estimation of large microphone arrays of arbitrary shape. Largeness is intended here in both number of microphones (hundreds) and size (few meters). Such arrays can be used for various applications in open or confined spaces like acoustical imaging, source identification, or speech processing. For so large array systems,measuring the geometry by hand is impractical. Therefore a blind passive method is proposed. It is based on the analysis of the background acoustic noise, supposed to be a diffuse field. The proposed strategy is a two-step process. First the pairwise microphone distances are identified by matching their measured coherence function to the one predicted by the diffuse field theory. Second, a robust multidimensional scaling(MDS) algorithm is adapted and implemented. It takes advantage of local characteristics to reduce the set of distances and infer the geometry of the array. This work is an extension of previous studies, and it overcomes unsolved drawbacks. In particular it deals efficiently with the outliers known to ruin standard MDS algorithms. Experimental proofs of this ability are presented by treating the case of two arrays. They show that the proposed improvements manage large spatial arrays.