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Room geometry inference (RGI) aims at estimating room shapes from measuredroom impulse responses (RIRs) and has received lots of attention for itsimportance in environment-aware audio rendering and virtual acousticrepresentation of a real venue. A lot of estimation models utilizing timedifference of arrival (TDoA) or time of arrival (ToA) information in RIRs havebeen proposed. However, an estimation model should be able to handle moregeneral features and complex relations between reflections to cope with variousroom shapes and uncertainties such as the unknown number of walls. In thisstudy, we propose a deep neural network that can estimate various room shapeswithout prior assumptions on the shape or number of walls. The proposed modelconsists of three sub-networks: a feature extractor, parameter estimation, andevaluation networks, which extract key features from RIRs, estimate parameters,and evaluate the confidence of estimated parameters, respectively. The networkis trained by about 40,000 RIRs simulated in rooms of different shapes using asingle source and spherical microphone array and tested for rooms of unseenshapes and dimensions. The proposed algorithm achieves almost perfect accuracyin finding the true number of walls and shows negligible errors in room shapes.

3D Room Geometry Inference from Multichannel Room Impulse Response using Deep Neural Network