  Deep neural network (DNN)-based speech enhancement algorithms in microphone
arrays have now proven to be efficient solutions to speech understanding and
speech recognition in noisy environments. However, in the context of ad-hoc
microphone arrays, many challenges remain and raise the need for distributed
processing. In this paper, we propose to extend a previously introduced
distributed DNN-based time-frequency mask estimation scheme that can
efficiently use spatial information in form of so-called compressed signals
which are pre-filtered target estimations. We study the performance of this
algorithm under realistic acoustic conditions and investigate practical aspects
of its optimal application. We show that the nodes in the microphone array
cooperate by taking profit of their spatial coverage in the room. We also
propose to use the compressed signals not only to convey the target estimation
but also the noise estimation in order to exploit the acoustic diversity
recorded throughout the microphone array.
