  Knowing the geometrical and acoustical parameters of a room may benefit
applications such as audio augmented reality, speech dereverberation or audio
forensics. In this paper, we study the problem of jointly estimating the total
surface area, the volume, as well as the frequency-dependent reverberation time
and mean surface absorption of a room in a blind fashion, based on two-channel
noisy speech recordings from multiple, unknown source-receiver positions. A
novel convolutional neural network architecture leveraging both single- and
inter-channel cues is proposed and trained on a large, realistic simulated
dataset. Results on both simulated and real data show that using multiple
observations in one room significantly reduces estimation errors and variances
on all target quantities, and that using two channels helps the estimation of
surface and volume. The proposed model outperforms a recently proposed blind
volume estimation method on the considered datasets.
