  We present an approach to deep neural network based (DNN-based) distance
estimation in reverberant rooms for supporting geometry calibration tasks in
wireless acoustic sensor networks. Signal diffuseness information from acoustic
signals is aggregated via the coherent-to-diffuse power ratio to obtain a
distance-related feature, which is mapped to a source-to-microphone distance
estimate by means of a DNN. This information is then combined with
direction-of-arrival estimates from compact microphone arrays to infer the
geometry of the sensor network. Unlike many other approaches to geometry
calibration, the proposed scheme does only require that the sampling clocks of
the sensor nodes are roughly synchronized. In simulations we show that the
proposed DNN-based distance estimator generalizes to unseen acoustic
environments and that precise estimates of the sensor node positions are
obtained.
