  A wireless acoustic sensor network records audio signals with sampling time
and sampling rate offsets between the audio streams, if the analog-digital
converters (ADCs) of the network devices are not synchronized. Here, we
introduce a new sampling rate offset model to simulate time-varying sampling
frequencies caused, for example, by temperature changes of ADC crystal
oscillators, and propose an estimation algorithm to handle this dynamic aspect
in combination with changing acoustic source positions. Furthermore, we show
how deep neural network based estimates of the distances between microphones
and human speakers can be used to determine the sampling time offsets. This
enables a synchronization of the audio streams to reflect the physical time
differences of flight.
