Calibration of Multiple Asynchronous Microphone Arrays using Hybrid TDOA

2025-02-10

AI Summary

High-precision joint calibration of multiple asynchronous microphone arrays remains challenging due to coupled spatial and temporal uncertainties. Method: This paper proposes a two-stage calibration framework. Stage I jointly fuses inter-array TDOA (TDOA-M) and inter-event TDOA (TDOA-S), integrated with robot odometry and initial DOA estimates, to recover coarse array poses and clock parameters. Stage II performs collaborative estimation of positions, orientations, clock offsets, and drift rates via hybrid TDOA modeling, ICP-based directional optimization, and nonlinear joint least-squares refinement. Contribution/Results: To our knowledge, this is the first work unifying TDOA-M and TDOA-S constraints within a single coherent model, significantly enhancing calibration robustness and accuracy. Extensive simulations and real-world experiments demonstrate that, under low-to-moderate TDOA noise, parameter estimation errors are reduced by 30–50% compared to state-of-the-art methods—establishing a more reliable geometric and temporal foundation for acoustic source localization and tracking.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Calibration & Uncertainty Quantification
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Calibration of asynchronous microphone arrays
• Enhancing sound source localization accuracy
• Incorporating hybrid TDOA measurements for calibration

Innovation

• Hybrid TDOA calibration
• Two-stage approach
• Joint optimization method