Research interests

Bayesian Computational Methods for Signal Processing

Underpinning much of our work is the Bayesian paradigm and associated algorithms for inference about the parameters and structure of complex systems. In the Bayesian approach data is combined with any prior information available in an optimal fashion using probability distributions. We are particularly concerned with the development of new methods appropriate to the applications above. These applications are often sequential in nature (the data arrive one-by-one and a decision/estimate is required with small or no delay), hence we focus considerable attention on sequential learning methods such as Sequential Monte Carlo (particle filtering). Other problems are batch in nature (the data arrive all at once, or we can wait until all of the data have arrived before processing) – in those cases batch algorithms can be used, and we focus attention on stochastic simulation methods such as Markov chain Monte Carlo (MCMC), including those for model uncertainty problems (reversible jump MCMC, etc.). Novel techniques are developed to help tailor these methods to the applications at hand.

Audio and Music Processing (AMP)

The Signal Processing Laboratory has had long involvement in audio and music processing. Early work in sound restoration here in the 1980’s led to the founding of the successful company CEDAR Audio Ltd. which produces DSP equipment for remastering and enhancement of sound in the recording, broadcast and forensic industries. In current research we are concerned with accurate modelling of digital audio and automated inference about the parameters and structure of those models. Research interests include computer music transcription, audio source separation, musical beat-tracking, chord recognition, Digital Audio Restoration, noise reduction, multichannel audio and sparse modelling with overcomplete dictionaries of atoms. Underpinning much of the work is a Bayesian statistical modelling approach to audio problems.

Tracking Algorithms

A major challenge in many application areas is that of detection, classification and tracking of multiple objects. Classic applications of this include radar and sonar, but the principles extend into computer vision, robotics and many other areas. We are aiming to push back the boundaries of current technology where many objects are present, detection probabilities are low and clutter rates are high. The methods devised use novel implementations of Monte Carlo Bayesian updating to carry out joint detection of number, characteristics and position of objects in cluttered environments.

Teaching activities

• 3F3 Signal and Pattern processing
• IB Paper 6 – Signal and Data Analysis

Role and responsibilities

Simon Godsill is Head of Information Engineering (Div F), and Professor of Statistical Signal Processing in the Engineering Department at Cambridge University. He is also a Professorial Fellow at Corpus Christi College Cambridge. He coordinates an active research group in Signal Inference and its Applications, specializing in Bayesian computational methodology, multiple object tracking, spatio-temporal inference, audio and music processing, and stochastic process simulation/inference. A particular methodological theme over recent years has been the development of novel techniques for optimal Bayesian filtering and smoothing, using computastional methods such as Sequential Monte Carlo (SMC), Particle Filtering methods, Markov Chain Monte Carlo (MCMC), Variational Bayes and Expectation-Maximisation (EM).