  Blind acoustic parameter estimation consists in inferring the acoustic
properties of an environment from recordings of unknown sound sources. Recent
works in this area have utilized deep neural networks trained either partially
or exclusively on simulated data, due to the limited availability of real
annotated measurements. In this paper, we study whether a model purely trained
using a fast image-source room impulse response simulator can generalize to
real data. We present an ablation study on carefully crafted simulated training
sets that account for different levels of realism in source, receiver and wall
responses. The extent of realism is controlled by the sampling of wall
absorption coefficients and by applying measured directivity patterns to
microphones and sources. A state-of-the-art model trained on these datasets is
evaluated on the task of jointly estimating the room's volume, total surface
area, and octave-band reverberation times from multiple, multichannel speech
recordings. Results reveal that every added layer of simulation realism at
train time significantly improves the estimation of all quantities on real
signals.
