AN AUTOMATIC SYSTEM FOR MICROPHONE SELF-LOCALIZATION USING AMBIENT SOUND

Abstract:
In this paper, we develop a system for microphone selflocalization

based on ambient sound, without any assumptions

on the 3D locations of the microphones and sound

sources. We aim at developing a system capable of dealing

with multiple moving sound sources. We will show that this

is possible given that there are instances where there are only

one dominating sound source. In the first step of the system

we employ a feature detection and matching strategy. This

produces TDOA data, possibly with missing data and with

outliers. Then we use a robust and stratified approach for the

parameter estimation. We use robust techniques to calculate

initial estimates on the offsets parameters, followed by nonlinear

optimization based on a rank criterion. Sequentially

we use robust methods for calculating initial estimates of the

sound source positions and microphone positions, followed

by non-linear Maximum Likelihood estimation of all parameters.

The methods are tested and verified using anechoic

chamber sound recordings.