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Simon Godsill                     Small Photo       

 

Signal Processing Laboratory

 

 

Professor of Statistical Signal Processing 

Fellow of Corpus Christi College Cambridge

 

 

 

 

Research Interests - Signal Inference and its Applications:

     























 

 

 

  

 

 

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.

   Researchers (current and recent past): Taylan Cemgil, Paul Peeling, Nick Whitely, Han Lin, Maurice Fallon,  Cédric Févotte, William Fong, Simon Godsill, Steve Hainsworth, Matt Scarisbrick, Jaco Vermaak, Patrick Wolfe, Manuel Davy

 

   Selected papers:

 

ˇ        C. Févotte and S. Godsill. Sparse linear regression in unions of bases via Bayesian variable selection. IEEE Signal Processing Letters, 13(7):441-444, Jul. 2006.
[ bib | http ]

ˇ        P. H. Peeling, C. Li, and S. J. Godsill. Poisson point process modeling for polyphonic music transcription. Journal of the Acoustical Society of America Express Letters, 121(4):EL168-EL175, April 2007. Reused with permission from Paul Peeling, The Journal of the Acoustical Society of America, 121, EL168 (2007). Copyright 2007, Acoustical Society of America.[ bib | .html | .ps.gz | .pdf ]

ˇ        T. Cemgil, S. J. Godsill, and C. Fevotte. Variational and Stochastic Inference for Bayesian Source Separation. Digital Signal Processing, Accepted for Publication, 2007.
[ bib | .pdf ]

ˇ         Févotte and S.J. Godsill. A Bayesian approach for blind separation of sparse sources. IEEE Trans. on Speech and Audio Processing, 2007.
[ bib ]

ˇ        M. Davy, S.J. Godsill, and J. Idier. Bayesian analysis of polyphonic western tonal music. Journal of the Acoustical Society of America, 119(4), April 2006.

ˇ        P.J. Wolfe and S. J. Godsill. Interpolation of missing data values for audio signal restoration using a Gabor regression model. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing, March 2005.

ˇ        C. Févotte and S. J. Godsill. A Bayesian approach to time-frequency based blind source separation. In Proc. of IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, October 2005.

ˇ        M. Lombardi and S.J. Godsill. On-line Bayesian estimation of AR signals in symmetric alpha-stable noise. IEEE Trans. on Signal Processing, 2006. [ bib | .html]

ˇ        P. J. Wolfe, S. J. Godsill, and W.J. Ng. Bayesian variable selection and regularisation for time-frequency surface estimation. Journal of the Royal Statistical Society, Series  B 66(3):575-589, 2004. Read paper (with discussion). [bib | .pdf ]

ˇ         M.Davy and S. J. Godsill. Bayesian harmonic models for musical signal analysis (with discussion). In J.M. Bernardo, J.O. Berger, A.P. Dawid, and A.F.M. Smith, editors, Bayesian Statistics VII. Oxford University Press, 2003. [ bib | .ps ]

ˇ         S. J. Godsill and P. J. W. Rayner. Robust reconstruction and analysis of autoregressive signals in impulsive noise using the Gibbs sampler. IEEE Trans. on Speech and Audio Processing, 6(4):352-372, July 1998. [ bib | .ps.gz ]
 

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


Talks:  
   
             Bayesian harmonic models for musical signal analysis (with M. Davy). Invited lecture for Seventh Valencia conference on Bayesian Statistics - Tenerife, 2002

 

          Digital Audio Restoration. An introductory talk given at Helsinki University of Technology, Fall 2003

            An introduction to MCMC methods for sparse overcomplete sparse audio models. Tutorial for European Union Project HASSIP workshop, Cambridge, Sept. 2004

Recent 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.

Researchers (current and recent past):  Sze Kim Pang, Daniel Clark, Jaco Vermaak, William Ng, Jack Li, Pieter J.P. de Villiers

Selected papers

 

ˇ         S.J. Godsill, J. Vermaak, K-F. Ng, and J-F. Li. Models and algorithms for tracking of manoeuvring objects using variable rate particle filters. Proc. IEEE, May 2007. [ bib ]

ˇ        S.J. Godsill. Particle filters for continuous-time jump models in tracking applications. In ESAIM: PROCEEDINGS of Oxford Workshop on Particle Filtering, 2007. (To Appear). [ bib ]       

ˇ        J. Vermaak, N. Ikoma, and S.J. Godsill. Sequential Monte Carlo framework for extended object tracking. IEE Proc.-Radar Sonar Navig., 152(5):353-363, October 2005.

ˇ        J. Vermaak, S. Godsill, and P. Perez. Monte Carlo filtering for multi-target tracking and data association. IEEE Tr. Aerospace and Electronic Systems, 41(1):309-332, January 2005.

ˇ        K. Gilholm, S.J. Godsill, S. Maskell, and D. Salmond. Poisson models for extended target and group tracking. In Proc. SPIE: Signal and Data Processing of Small Targets, 2005.

ˇ        S. J. Godsill and J. Vermaak. Variable rate particle filters for tracking applications. In Proc. IEEE Stat. Sig. Proc., Bordeaux, 2005.

ˇ        W. Ng, J.F. Li, S.J. Godsill, and J. Vermaak. A hybrid approach for online joint detection and tracking for multiple targets. In IEEE Aerospace Conference, 2005.

ˇ        S. J. Godsill and J. Vermaak,  Models and algorithms for tracking using trans-dimensional sequential Monte Carlo. In Proc. IEEE ICASSP 2004

 


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:

ˇ          Ji Won Yoon, Simon Godsill, Eriks Kupce, and Ray Freeman. Deterministic and statistical methods for reconstructing multidimensional nmr spectra. Magnetic Resonance in Chemistry, March 2006. [ bib ]

ˇ         N. M. Haan and S. J. Godsill. Bayesian models for DNA sequencing. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing, 2002

ˇ         N. M. Haan and S. J. Godsill. A time-varying model for DNA sequencing data submerged in correlated noise. In Proc. IEEE Workshop on Statistical Signal Processing, August 2001

ˇ         N.M. Haan and S.J. Godsill. Sequential methods for DNA sequencing. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing, 2001.                           

ˇ         N. M. Haan and S. J. Godsill. Modelling electropherogram data for DNA sequencing using variable dimension MCMC. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing, 2000.

                            

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.

   Projects: Bayesian inference for partially oberved diffusion models - EPSRC (in collaboration with Gareth Roberts and Neil Shephard) -  researcher Gary Yang

   Selected papers:

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

ˇ        P. J. Wolfe, S. J. Godsill, and W.J. Ng. Bayesian variable selection and regularisation for time-frequency surface estimation. Journal of the Royal Statistical Society, Series B, 2004. Read paper (with discussion).

ˇ        J. Vermaak, C. Andrieu, A. Doucet, and S. J. Godsill. Bayesian model selection of autoregressive processes. J. Time Series Anal. (In Press).

ˇ        S. J. Godsill. Discussion of `trans-dimensional Markov chain Monte Carlo' by Peter J. Green. In Highly Structured Stochastic Systems. OUP, 2003.

ˇ        Doucet, S. J. Godsill, and C. P. Robert. Marginal maximum a posteriori estimation using MCMC. Statistics and Computing, 12:77-84, 2002.         

ˇ        S. J. Godsill. On the relationship between Markov chain Monte Carlo methods for model uncertainty. J. Comp. Graph. Stats., 10(2):230-248, 2001.         

ˇ        Paul T. Troughton and Simon J. Godsill. MCMC methods for restoration of nonlinearly distorted autoregressive signals. Signal Processing, 81(1):83-97, 2001.

ˇ        S. J. Godsill. Inference in symmetric alpha-stable noise using MCMC and the slice sampler. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing, volume VI, pages 3806-3809, 2000.

 

ˇ        S. J. Godsill. MCMC and EM-based methods for inference in heavy-tailed processes with alpha-stable innovations. In Proc. IEEE Signal processing workshop on higher-order statistics, June 1999. Caesarea, Israel.

ˇ        S. J. Godsill and E. E. Kuruoglu. Bayesian inference for time series with heavy-tailed symmetric alpha -stable noise processes. CUED Tech rep INFENG...In Proc. Applications of heavy tailed distributions in economics, engineering and statistics, June 1999. Washington DC, USA.         

     Sequential Monte Carlo (particle filtering) methods:

 

ˇ         O. Cappé, S.J. Godsill, and E.Moulines. An overview of existing methods and recent advances in sequential monte carlo. Proc. IEEE, May 2007.[ bib ]
S.J. Godsill, J. Vermaak, K-F. Ng, and J-F. Li. Models and algorithms for tracking of manoeuvring objects using variable rate particle filters. Proc. IEEE, May 2007.[ bib ]

ˇ         M. Lombardi and S.J. Godsill. On-line Bayesian estimation of AR signals in symmetric alpha-stable noise. IEEE Trans. on Signal Processing, 2006. [ bib | .html]

ˇ         S. J. Godsill and J. Vermaak,  Models and algorithms for tracking using trans-dimensional sequential Monte Carlo. In Proc. IEEE ICASSP 2004

ˇ         S.J. Godsill and A. Doucet and M. West. Monte Carlo smoothing for non-linear time series. Journal of the American Statistical Association. Vol.50, pp. 438-449, 2004

ˇ         J. Vermaak, S. J. Godsill, and A. Doucet. Radial basis function regression using trans-dimensional sequential Monte Carlo. In IEEE Workshop on Statistical Signal Processing, 2003.

ˇ         J. Vermaak, S. J. Godsill, and A. Doucet. Sequential Bayesian kernel regression. In Advances in Neural Information Processing Systems 16, Cambridge, MA. MIT Press, 2003.

ˇ         W. Fong, S. J. Godsill, A. Doucet, and M. West. Monte Carlo smoothing with application to speech enhancement. IEEE Trans. on Signal Processing, 50(2):438-449, February 2002.            

ˇ         J. Vermaak, C. Andrieu, A. Doucet, and S. J. Godsill. Particle methods for Bayesian modelling and enhancement of speech signals. IEEE Trans. on Speech and Audio Processing,      10(3):173-185, 2002.

ˇ          S. J. Godsill and T. C. Clapp. Improvement strategies for Monte Carlo particle filters. In A Doucet, J. F. G. De Freitas, and N. J. Gordon, editors, Sequential Monte Carlo Methods in Practice. New York: Springer-Verlag, 2001.

ˇ         S. J. Godsill, A Doucet, and M West. Maximum a posteriori sequence estimation using Monte Carlo particle filters. Ann. Inst. Stat. Math., 53(1):82-96, March 2001.                                    

ˇ         Doucet, S. J. Godsill, and C. Andrieu. On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing, 10:197-208, 2000.

ˇ         T. C. Clapp and S. J. Godsill. Fixed-lag smoothing using sequential importance sampling. In J.M. Bernardo, J.O. Berger, A.P. Dawid, and A.F.M. Smith, editors, Bayesian Statistics VI, pages 743-752. Oxford University Press

 

Tutorial Materials:


Bayesian Computer intensive methods for Statistical Signal  Processing (Plenary Address, IEEE Workshop on Statistical Signal Processing, St Louis August 2003)

On-Line Bayesian Methods for estimation of non-linear non-Gaussian signals  (Tutorial for Opening Workshop of SAMSI programme, North Carolina, Sept. 2002)

 

 

Teaching

Current activities include:


        4F7 - Adaptive Filters and Spectrum Estimation
        3F3 - Signal and Pattern processing
        IB Paper 7 - Mathematics -  Signal and Data Analysis

 

 

Full list of Publications:

Book  - Digital Audio Restoration - a statistical model-based approach (Springer-Verlag 1998)

Papers (including downloadable Postscript/pdf)

Bayesian Picture Gallery

 


[Cambridge University | CUED |Signal Processing Group

Updated Sept. 07
 

 
 
sjg@eng.cam.ac.uk