  Classical methods for acoustic scene mapping require the estimation of time
difference of arrival (TDOA) between microphones. Unfortunately, TDOA
estimation is very sensitive to reverberation and additive noise. We introduce
an unsupervised data-driven approach that exploits the natural structure of the
data. Our method builds upon local conformal autoencoders (LOCA) - an offline
deep learning scheme for learning standardized data coordinates from
measurements. Our experimental setup includes a microphone array that measures
the transmitted sound source at multiple locations across the acoustic
enclosure. We demonstrate that LOCA learns a representation that is isometric
to the spatial locations of the microphones. The performance of our method is
evaluated using a series of realistic simulations and compared with other
dimensionality-reduction schemes. We further assess the influence of
reverberation on the results of LOCA and show that it demonstrates considerable
robustness.
