Passive Self-Localization of Microphones Using Ambient Sounds

This work presents a method to localize a set of microphones using recorded signals from surrounding continuous sounds such as speech. When a sound wave travels through a microphone array a time difference of arrival (TDOA) can be extracted between each microphone pair. A sound wave impinging towards a microphone pair from the end-fire direction results in the extreme TDOA value, leading to information about microphone distance. In indoors the reverberation may cause TDOA outliers, and a set of non-linear techniques for estimating the distance is proposed. The multidimensional scaling (MDS) is used to map the microphone pairwise distances into Cartesian microphone locations. The accuracy of the method and the effect of the number of sources is evaluated using speech signals in simulated environment. A self-localization RMS error of 7 cm was reached using ten asynchronous smartphones in a meeting room from a recorded conversation with a maximum of 3.7 m device separation.

Microphone arrays; Array Shape Calibration; Self-Localization; TDOA estimation; Multidimensional Scaling; self localization