Closed-Form Self-Localization of Asynchronous Microphone Arrays

The utilization of distributed microphone arrays in many speech processing applications such as beamforming and speaker localization rely on the precise knowledge of microphone locations. Several self- localization approaches have been presented in the literature but still a simple, accurate, and robust method for asynchronous devices is lacking. This work presents an analytical solution for estimating the positions and rotations of asynchronous loudspeaker equipped microphone arrays or devices. The method is based on emitting and receiving calibration signals from each device, and extracting the time of arrival (TOA) values. Utilizing the knowledge of array geometry in the TOA estimation is proposed to improve accuracy of translation. Results with measurements using four devices on a table surface demonstrates a mean translation error of 11 mm with standard deviation of 6 mm and mean z-axis rotation error of 0.11 (rad) with a standard deviation of 0.14 (rad) in contrast to computer vision annotations with 200 rotations and translation estimates.

self-localization; self localization; microphone arrays; calibra- tion; localization; computer vision