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.

翻译：在信号缺失环境下的非视距定位是一个具有挑战性且至关重要的问题。在室内场景为主的情况下，声学方法由于混响特性而面临困难。本研究旨在通过利用基于物理的声传播模拟和机器学习方法，将声源定位到虚拟环境中的特定位置。该过程试图克服数据不足的问题，尤其是在事后的声源定位中，将声源定位到其发生位置。我们采用基于音频变换器的频谱图方法，实现了0.786±0.0136的F1分数。