  In multichannel signal processing with distributed sensors, choosing the
optimal subset of observed sensor signals to be exploited is crucial in order
to maximize algorithmic performance and reduce computational load, ideally both
at the same time. In the acoustic domain, signal cross-correlation is a natural
choice to quantify the usefulness of microphone signals, i.e., microphone
utility, for array processing, but its estimation requires that the uncoded
signals are synchronized and transmitted between nodes. In resource-constrained
environments like acoustic sensor networks, low data transmission rates often
make transmission of all observed signals to the centralized location
infeasible, thus discouraging direct estimation of signal cross-correlation.
Instead, we employ characteristic features of the recorded signals to estimate
the usefulness of individual microphone signals. In this contribution, we
provide a comprehensive analysis of model-based microphone utility estimation
approaches that use signal features and, as an alternative, also propose
machine learning-based estimation methods that identify optimal sensor signal
utility features. The performance of both approaches is validated
experimentally using both simulated and recorded acoustic data, comprising a
variety of realistic and practically relevant acoustic scenarios including
moving and static sources.
