Slim Essid on Analysing Audio and EEG data with Nonnegative Matrix Factorisation
Slim Essid will be at CCRMA to talk about using NMF to analyze audio signals for auditory scene analysis, and to decompose EEG signals into their independent sources. Both important tasks.
Who: Slim Essid (Paris Telecom Institute)
What: Analysing Audio and EEG data with Nonnegative Matrix Factorisation
When: Friday 9/30 at 10:30AM
Where: CCRMA Seminar Room (top floor of the knoll)
Why: We want a better understanding of our signals.
Come to CCRMA for a very positive talk.
Title: Analysing Audio and EEG data with Nonnegative Matrix Factorisation
I will present an overview of research performed at the Audio group of Telecom ParisTech, focusing on contributions to Nonnegative Matrix Factorisation (NMF) and applications to audio and EEG data analysis. First I will briefly cover our recent work on feature learning with NMF where we have explored diverse algorithmic variations that allow us to achieve competitive classification results in audio scene analysis tasks. Then I will describe co-factorisation schemes for the analysis of temporally structured multimodal data (including EEG), where we have introduced original formulations and algorithms to perform smooth factorisation and soft co-factorisation. The former yields representations suitable for the analysis of temporal sequences, possibly with piecewise-constant activations, useful in many temporal segmentation tasks. As for the soft co-factorisation schemes, they allow for jointly performing two (or more) factorisations, so as to produce models capturing the dependencies that may exist between the modalities being analysed in parallel.
Slim Essid is an Associate Professor at the Department of Image and Signal Processing-TSI of TELECOM ParisTech and the head of the Audio group. His research interests are in machine learning for multimodal data analysis, with applications to machine perception, especially machine listening, and multimedia content analysis. He received the Ph.D. degree from the Université Pierre et Marie Curie (UPMC), in 2005; and the habilitation (HDR) degree from UPMC in 2015. He has been involved in various French and European research projects among which are Quaero, Networks of Excellence Kspace and 3DLife, and FP7 projects REVERIE and LASIE. Over the past 3 years, he has collaborated with 4 post-docs and has graduated 4 PhD students; he is currently supervising 8 others. He has published over 80 peer-reviewed conference and journal papers with more than 50 distinct co-authors. On a regular basis he serves as a reviewer for various signal processing, audio and multimedia conferences and journals, for instance various IEEE transactions, and as an expert for research funding agencies. More info at: http://perso.telecom-paristech.fr/~essid/