Physically Informed Spatial Regularization for Sound Event Localization and Detection

Building Sound Event Localization and Detection (SELD) models that are robust to diverse acoustic environments remains one of the major challenges in multichannel signal processing, as reflections and reverberation can significantly confuse both the source direction and event detection. Introducing priors such as microphone geometry or room impulse response (RIR) into the model has proven effective in addressing this issue. Existing methods typically incorporate such priors in a deterministic way, often through data augmentation to enlarge data diversity. However, the uncertainty arising from the complex nature of audio acoustics remains largely underexplored in the SELD literature and naturally call for incorporating a stochastic modeling of acoustic prior. In this paper, we propose regularizing deep learning based SELD models with a physically constructed spatial covariance matrix (SCM) based on the estimated direction of arrival (DOA) and sound event detection (SED).