  Non-line-of-sight localization in signal-deprived environments is a
challenging yet pertinent problem. Acoustic methods in such predominantly
indoor scenarios encounter difficulty due to the reverberant nature. In this
study, we aim to locate sound sources to specific locations within a virtual
environment by leveraging physically grounded sound propagation simulations and
machine learning methods. This process attempts to overcome the issue of data
insufficiency to localize sound sources to their location of occurrence
especially in post-event localization. We achieve 0.786+/- 0.0136 F1-score
using an audio transformer spectrogram approach.
