Audio Simulation for Sound Source Localization in Virtual Evironment
This addresses the challenge of data insufficiency for post-event localization in virtual environments, though it appears incremental as it applies existing methods to a specific domain.
The paper tackles the problem of non-line-of-sight sound source localization in reverberant indoor environments by using physically grounded audio simulations and machine learning, achieving an F1-score of 0.786 +/- 0.0136.
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.