  In this paper we introduce a realistic and challenging, multi-source and
multi-room acoustic environment and an improved algorithm for the estimation of
source-dominated microphone clusters in acoustic sensor networks. Our proposed
clustering method is based on a single microphone per node and on unsupervised
clustered federated learning which employs a light-weight autoencoder model. We
present an improved clustering control strategy that takes into account the
variability of the acoustic scene and allows the estimation of a dynamic range
of clusters using reduced amounts of training data. The proposed approach is
optimized using clustering-based measures and validated via a network-wide
classification task.
