  The inference of the absorption configuration of an existing room solely
using acoustic signals can be challenging. This research presents two methods
for estimating the room dimensions and frequency-dependent absorption
coefficients using room transfer functions. The first method, a knowledge-based
approach, calculates the room dimensions through damped resonant frequencies of
the room. The second method, a machine learning approach, employs multi-task
convolutional neural networks for inferring the room dimensions and
frequency-dependent absorption coefficients of each surface. The study shows
that accurate wave-based simulation data can be used to train neural networks
for real-world measurements and demonstrates a potential for this algorithm to
be used to estimate the boundary input data for room acoustic simulations. The
proposed methods can be a valuable tool for room acoustic simulations during
acoustic renovation or intervention projects, as they enable to infer the room
geometry and absorption conditions with reasonably small data requirements.
