Eric Grinstein

Deep Learning (DL) methods currently obtain state-of-the-art performance in the tasks of positional Sound Source Localization (SSL) and acoustic Direction of Arrival (DOA) estimation. However, most DL methods require matched microphone array geometries between training and testing scenarios, requiring separate models to be trained for different devices. In this webinar, the presenter will present geometry-aware and geometry-agnostic DL approaches for SSL, comparing their advantages and drawbacks, and future research directions.