Lecturer: Xavier Alameda-Pineda is a (tenured) Research Scientist at Inria, in the Perception Group. He obtained the M.Sc. (equivalent) in Mathematics in 2008, in Telecommunications in 2009 from BarcelonaTech and in Computer Science in 2010 from Université Grenoble-Alpes (UGA). He then worked towards his Ph.D. in Mathematics and Computer Science, and obtained it in 2013, from UGA. After a two-year post-doc period at the Multimodal Human Understanding Group, at University of Trento, he was appointed with his current position. Xavier is an active member of SIGMM, and a senior member of IEEE and a member of ELLIS. He is co-chairing the “Audio-visual machine perception and interaction for companion robots” chair of the Multidisciplinary Institute of Artificial Intelligence. Xavier is the Coordinator of the H2020 Project SPRING: Socially Pertinent Robots in Gerontological Healthcare. Xavier’s research interests are in combining machine learning, computer vision and audio processing for scene and behavior analysis and human-robot interaction. More info at xavirema.eu
Abstract: Since their introduction, Variational Autoencoders (VAE) demonstrated great performance in key unsupervised feature representation applications, specifically in visual and auditory representation. In this seminar, the global methodology of variational auto-encoders will be presented, along with applications in learning with audio and visual data. Special emphasis will be put in discussing the use of VAE for audio-visual learning, showcasing its interest with the task of audio-visual speech enhancement.
Variational Autoencoders for Audio, Visual and Audio-Visual Learning – Recording