Every month, the joint laboratory invites external speakers to take part in seminars for its partners.

Eric Grinstein (Rode Microphones, UK): “Geometry-aware Deep Learning methods for Sound Source Localization.”

Abstract:

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.

Bio:
Eric Grinstein received the B.S. degree in computer engineering from PUC-Rio, Brazil in 2019, the M.Eng. degree in electrical engineering at IMT Atlantique, France, in 2019, and the Ph.D. degree in electrical engineering at Imperial College London, U.K. in 2025. He is currently an AI Engineer at RODE Microphones. He previously held roles as Machine Learning Engineer at Bose Corporation, Research Scientist Intern at Meta Reality Labs, visiting researcher at KU Leuven, and Data Engineer at Microsoft.