  Room geometry inference (RGI) aims at estimating room shapes from measured
room impulse responses (RIRs) and has received lots of attention for its
importance in environment-aware audio rendering and virtual acoustic
representation of a real venue. A lot of estimation models utilizing time
difference of arrival (TDoA) or time of arrival (ToA) information in RIRs have
been proposed. However, an estimation model should be able to handle more
general features and complex relations between reflections to cope with various
room shapes and uncertainties such as the unknown number of walls. In this
study, we propose a deep neural network that can estimate various room shapes
without prior assumptions on the shape or number of walls. The proposed model
consists of three sub-networks: a feature extractor, parameter estimation, and
evaluation networks, which extract key features from RIRs, estimate parameters,
and evaluate the confidence of estimated parameters, respectively. The network
is trained by about 40,000 RIRs simulated in rooms of different shapes using a
single source and spherical microphone array and tested for rooms of unseen
shapes and dimensions. The proposed algorithm achieves almost perfect accuracy
in finding the true number of walls and shows negligible errors in room shapes.
