An automatic system for microphone self-localization using ambient sound

In this paper, we develop a system for microphone self-localization 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.