  Speech enhancement in ad-hoc microphone arrays is often hindered by the
asynchronization of the devices composing the microphone array.
Asynchronization comes from sampling time offset and sampling rate offset which
inevitably occur when the microphones are embedded in different hardware
components. In this paper, we propose a deep neural network (DNN)-based speech
enhancement solution that is suited for applications in ad-hoc microphone
arrays because it is distributed and copes with asynchronization. We show that
asynchronization has a limited impact on the spatial filtering and mostly
affects the performance of the DNNs. Instead of resynchronising the signals,
which requires costly processing steps, we use an attention mechanism which
makes the DNNs, thus our whole pipeline, robust to asynchronization. We also
show that the attention mechanism leads to the asynchronization parameters in
an unsupervised manner.
