Signal Processing and Communications Laboratory

Department of Engineering

Simon Godsill

Background - Research - Publications - Teaching

Position: Professor of Statistical Signal Processing

Office Location: BE3-09

Telephone: +44 1223 332604

E-mail: sjg [at] eng.cam.ac.uk

Dept. of Engineering,
University of Cambridge,
Trumpington Street,
Cambridge.
CB2 1PZ, UK

Background

Fellow of Corpus Christi College Cambridge.

Research Interests - Signal Inference and its Applications:


  • Tracking:
    sensor fusion, multiple object tracking, detection, radar, sonar.

  • Signal inference methodololgy:
    Bayesian methods, Monte Carlo methods, Markov chain Monte Carlo, Particle Filters (sequential Monte Carlo), model uncertainty.

Research Areas

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, see below.

Selected papers:

Book:

  • S.J. Godsill and P.J.W. Rayner. Digital Audio Restoration - a statistical model-based approach (Berlin: Springer-Verlag 1998)

Talks:

Previous Research Projects: HASSIP, MOUMIR, MUSCLE

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.

Selected papers

Research Projects with: DIF-DTC, QinetiQ

Genomic and Life Sciences Signal Processing

A further topic of great importance is the interpretation and analysis of genomic data - for example the sequencing of the human genome. Any improvements achievable in this area are likely to lead to improvements our understanding of genetics and in treatment for diseases such as cancer. Work to date has focused on improving the performance of DNA sequencing machines through very accurate Bayesian modelling. Currents topics of work include the accurate preprocessing of microarray data - crucial in identification of the genes active in certain diseases.

Selected papers:

Bayesian Computational Methods for Signal Processing

Underpinning much of the above applications 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.

Selected papers:

Markov Chain Monte Carlo (MCMC) methods, including model uncertainty:

Sequential Monte Carlo (particle filtering and smoothing) methods:

Tutorial Materials:

Teaching

Other links:


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Updated April 2013
sjg [at] eng.cam.ac.uk