Simon Godsill, Applications of statistical methods in computer music, tracking and finance
In this talk, he will give a survey of recent results of his research in the application of probabilistic methods, and in particular Bayesian statistical methods, to various areas, including automated understanding of musical signals, tracking of dynamically networked objects, and financial modelling.
- Date : 18/10/2012
- Lieu : CERLA
- Intervenants : Simon Godsill
- Organisateurs : Inria Lille - Nord Europe, LAGIS, IRCICA, LIFL
Who's Simon Godsill ?
Professor Simon Godsill coordinates an active research group in Signal Inference and its Applications within the Signal Processing and Communications Laboratory at the University of Cambridge. He specialises in Bayesian computational methodology, multiple object tracking, audio and music processing, and financial time series modelling.
He was Technical Chair of the 2006 IEEE NSSPW workshop on sequential and nonlinear filtering methods and has served as Associate Editor for IEEE Tr. Signal Processing and the Bayesian Analysis journal.
The Signal Processing Laboratory is involvement in audio and music processing, its current research is concerned with accurate modelling of digital audio and automated inference about the parameters and structure of those models.
Professor Godsills current research interests include
- Audio signal processing – source separation, music analysis and transcription, noise reduction, audio restoration, multiple channel audio and sparse/overcomplete models
- Tracking – sensor function, multiple object tracking, detection, radar and sonar
- Signal inference methodology – Bayesian and Monte Carlo methods, particulate filter and model uncertainty
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