Calibration of Multiple Asynchronous Microphone Arrays using Hybrid TDOA

TL;DR

This work tackles the challenge of calibrating multiple asynchronous microphone arrays for accurate 3D sound source localization. It introduces a two-stage framework that first estimates rough parameters using hybrid TDOA ($T^{M}$ and $T^{S}$), DOA, and odometer data (IVE) with ICP-based orientation recovery, then refines all parameters via a final joint Gauss-Newton optimization that jointly estimates array positions, orientations, time offsets, clock drifts, and source locations. Through simulations and real-world indoor experiments, the method demonstrates improved accuracy over state-of-the-art approaches at low to moderate $TDOA$ noise, with code and data publicly available. The approach advances practical multi-array calibration for robust acoustic sensing in complex environments.

Abstract

Accurate calibration of acoustic sensing systems made of multiple asynchronous microphone arrays is essential for satisfactory performance in sound source localization and tracking. State-of-the-art calibration methods for this type of system rely on the time difference of arrival and direction of arrival measurements among the microphone arrays (denoted as TDOA-M and DOA, respectively). In this paper, to enhance calibration accuracy, we propose to incorporate the time difference of arrival measurements between adjacent sound events (TDOAS) with respect to the microphone arrays. More specifically, we propose a two-stage calibration approach, including an initial value estimation (IVE) procedure and the final joint optimization step. The IVE stage first initializes all parameters except for microphone array orientations, using hybrid TDOA (i.e., TDOAM and TDOA-S), odometer data from a moving robot carrying a speaker, and DOA. Subsequently, microphone orientations are estimated through the iterative closest point method. The final joint optimization step estimates multiple microphone array locations, orientations, time offsets, clock drift rates, and sound source locations simultaneously. Both simulation and experiment results show that for scenarios with low or moderate TDOA noise levels, our approach outperforms existing methods in terms of accuracy. All code and data are available at https://github.com/AISLABsustech/Hybrid-TDOA-Multi-Calib.

Figures (3)

• Figure 1: The calibration scenario for multiple microphone arrays using hybrid TDOA information. Two microphone arrays (the 1-th and i-th) and two sound events (the j-th and j+1-th) are used for illustration here.
• Figure 2: Comparison of simulation results between our IVE method/whole approach and wang's IVE method/whole approach under three levels TDOA noises ($\sigma_{tdoa}=0.05ms,0.1ms,0.5ms$) and three levels DOA noises ($\sigma_{doa}=0.05ms,0.1ms,0.5ms$). Four metrics are used: average RMSE of microphone array locations (Loc. err. (cm)), orientations (Angle err. (°)), time offsets (Off. err. ($10^{-4}s$)) and clock drift rates (Dri. err. ($\mu s$)). In this figure, "A/B" means that A is our IVE method or our whole approach, and B is that of wang. Bold or gray-filled area means better.
• Figure 3: The multiple microphone arrays calibration scenario for real-world experiments.