Spatially-Aware Losses for Enhanced Neural Acoustic Fields

For immersive audio experiences, it is essential that sound propagation is accurately modeled from a source to a listener through space. For human listeners, binaural audio characterizes the acoustic environment, as well as the spatial aspects of an acoustic scene. Recent advancements in neural acoustic fields have demonstrated spatially continuous models that are able to accurately reconstruct binaural impulse responses for a given source/listener pair. Despite this, these approaches have not explicitly examined or evaluated the quality of these reconstructions in terms of the inter-aural cues that define spatialization for human listeners. In this work, we propose extending neural acoustic field-based methods with spatially-aware metrics for training and evaluation to better capture spatial acoustic cues. We develop a dataset based on the existing SoundSpaces dataset to better model these features, and we demonstrate performance improvements by utilizing spatially-aware losses.