  Distributed Microphone Arrays (DMAs) present many challenges with respect to
centralized microphone arrays. An important requirement of applications on
these arrays is handling a variable number of input channels. We consider the
use of Graph Neural Networks (GNNs) as a solution to this challenge. We present
a localization method using the Relation Network GNN, which we show shares many
similarities to classical signal processing algorithms for Sound Source
Localization (SSL). We apply our method for the task of SSL and validate it
experimentally using an unseen number of microphones. We test different feature
extractors and show that our approach significantly outperforms classical
baselines.
