Simon Godsill
Signal
Processing and Communications (SigProC) Laboratory
Professor of
Statistical Signal Processing
Fellow of Corpus Christi College Cambridge
News: research positions soon available in EPSRC project BTaRoT (Bayesian Tracking and Reasoning over time) and Audio Enhancement
Research Interests - Signal Inference and its Applications:
- Audio signal processing: source separation, music analysis and transcription, noise reduction, audio
restoration,
multiple channel audio, sparse/overcomplete
models
- Tracking: sensor fusion, multiple object tracking,
detection, radar, sonar
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- 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:
Fallon, MF and Godsill, SJ (2012) Acoustic Source Localization and Tracking of a Time-Varying Number of Speakers. IEEE T AUDIO SPEECH, 20. pp. 1409-1415. ISSN 1558-7916
Nielsen, JK and Christensen, MG and Cemgil, AT and Godsill, SJ and Jensen, SH (2011) Bayesian Interpolation and Parameter Estimation in a Dynamic Sinusoidal Model. IEEE T AUDIO SPEECH, 19. pp. 1986-1998. ISSN 1558-7916
Christensen, JEN and Godsill, SJ (2011) Bayesian classification of acoustical waveforms under environmental variability. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics. pp. 281-284.
Peeling, PH and Godsill, SJ (2011) Multiple pitch estimation using non-homogeneous poisson processes. IEEE Journal on Selected Topics in Signal Processing, 5. pp. 1133-1143. ISSN 1932-4553
Godsill, SJ (2010) The shifted inverse-gamma model for noise floor estimation in archived audio recordings. Signal Processing, 90. pp. 991-999. ISSN 0165-1684
Fallon, M and Godsill, SJ (2009) Acoustic source localisation and tracking using track before detect. IEEE Transactions on Audio, Speech and Language Processing, PP. p. 1. ISSN 1558-7916
Peeling, PH and Cemgil, AT and Godsill, SJ (2009) Generative spectrogram factorisation models for polyphonic piano transcription. IEEE Transactions on Audio, Speech and Language Processing, 18. pp. 519-527. ISSN 1558-7916
Fevotte, C and Torresani, B and Daudet, L and Godsill, SJ (2007) Sparse linear regression with structured priors and application to denoising of musical audio. IEEE Transactions on Audio Speech and Language Processing, 16. pp. 174-185. ISSN 1558-7916
Cemgil, AT and Godsill, SJ and Fevotte, C (2007) Variational and stochastic inference for Bayesian source separation. Digital Signal Processing, 17. pp. 891-913. ISSN 1051-2004
Davy, M and Godsill, SJ (2006) Bayesian analysis of polyphonic western tonal music. Journal of the Acoustical Society of America, 119. pp. 2498-2517. ISSN 0001-4966
Fevotte, C and Godsill, SJ (2006) A Bayesian approach for blind separation of sparse sources. IEEE Transactions on Audio, Speech and Language Processing, 14. pp. 2174-2188. ISSN 1558-7916
Lombardi, MJ and Godsill, SJ (2006) On-line Bayesian estimation of signals in symmetric /spl alpha/-stable noise. IEEE Transactions on Signal Processing, 54. pp. 775-779. ISSN 1053-587X
Fevotte, C and Godsill, SJ (2006) Sparse linear regression in unions of bases via Bayesian variable selection. IEEE Signal Processing Letters, 13. pp. 441-444. ISSN 1070-9908
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 ]
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]
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
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
Bunch, P and Godsill, SJ (2013) Particle Smoothing Algorithms for Variable Rate Models. IEEE Transactions on Signal Processing, 61. pp. 1663-1675.
Carmi, A and Septier, F and Godsill, SJ (2012) The Gaussian mixture MCMC particle algorithm for dynamic cluster tracking. Automatica, 48. pp. 2454-2467. ISSN 0005-1098
Pang, SK and Li, J and Godsill, SJ (2011) Detection and Tracking of Coordinated Groups. IEEE T AERO ELEC SYS, 47. pp. 472-502. ISSN 0018-9251
de Villiers, JP and Godsill, SJ and Singh, SS (2011) Particle predictive control. Journal of Statistical Planning and Inference, 141. pp. 1753-1763. ISSN 0378-3758
Whiteley, NP and Singh, SS and Godsill, SJ (2010) Auxiliary particle implementation of the probability hypothesis density filter. IEEE Transactions on Aerospace and Electronic Systems, 46. pp. 1437-1454. ISSN 0018-9251
Whiteley, NP and Johansen, AM and Godsill, SJ (2010) Monte Carlo filtering of piecewise deterministic processes. Journal of Computational and Graphical Statistics. pp. 1-21. ISSN 1061-8600
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 ]
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.
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.
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.
S. J. Godsill and J. Vermaak. Variable rate particle filters for tracking applications. In Proc. IEEE Stat. Sig. Proc., Bordeaux, 2005.
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.
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:
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 ]
N. M. Haan and S. J. Godsill. Bayesian models for DNA sequencing. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing, 2002
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
N.M. Haan and S.J. Godsill. Sequential methods for DNA sequencing. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing, 2001.
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.
Selected papers:
Markov Chain Monte Carlo (MCMC) methods, including model uncertainty:
Lemke, T and Godsill, SJ (2012) Linear gaussian computations for near-exact Bayesian Monte Carlo inference in skewed alpha-stable time series models. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. pp. 3737-3740. ISSN 1520-6149
Lemke, T and Godsill, SJ (2011) Enhanced poisson sum representation for alpha-stable processes. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. pp. 4100-4103. ISSN 1520-614
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).
J. Vermaak, C. Andrieu, A. Doucet, and S. J. Godsill. Bayesian model selection of autoregressive processes. J. Time Series Anal. (In Press).
S. J. Godsill. Discussion of `trans-dimensional Markov chain Monte Carlo' by Peter J. Green. In Highly Structured Stochastic Systems. OUP, 2003.
Doucet, S. J. Godsill, and C. P. Robert. Marginal maximum a posteriori estimation using MCMC. Statistics and Computing, 12:77-84, 2002.
S. J. Godsill. On the relationship between Markov chain Monte Carlo methods for model uncertainty. J. Comp. Graph. Stats., 10(2):230-248, 2001.
Paul T. Troughton and Simon J. Godsill. MCMC methods for restoration of nonlinearly distorted autoregressive signals. Signal Processing, 81(1):83-97, 2001.
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.
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.
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 and smoothing) methods:
Bunch, P and Godsill, SJ (2013) Particle Smoothing Algorithms for Variable Rate Models. IEEE Transactions on Signal Processing, 61. pp. 1663-1675.
Christensen, HL and Murphy, J and Godsill, SJ (2012) Forecasting high-frequency futures returns using online langevin dynamics. IEEE Journal on Selected Topics in Signal Processing, 6. pp. 366-380. ISSN 1932-4553
Carmi, A and Septier, F and Godsill, SJ (2012) The Gaussian mixture MCMC particle algorithm for dynamic cluster tracking. Automatica, 48. pp. 2454-2467. ISSN 0005-1098
Särkkä, S and Bunch, P and Godsill, SJ (2012) A backward-simulation based Rao-Blackwellized particle smoother for conditionally linear Gaussian models. IFAC Proceedings Volumes (IFAC-PapersOnline), 16. pp. 506-511. ISSN 1474-6670
Pang, SK and Li, J and Godsill, SJ (2011) Detection and Tracking of Coordinated Groups. IEEE T AERO ELEC SYS, 47. pp. 472-502. ISSN 0018-9251
de Villiers, JP and Godsill, SJ and Singh, SS (2011) Particle predictive control. Journal of Statistical Planning and Inference, 141. pp. 1753-1763. ISSN 0378-3758
Whiteley, NP and Singh, SS and Godsill, SJ (2010) Auxiliary particle implementation of the probability hypothesis density filter. IEEE Transactions on Aerospace and Electronic Systems, 46. pp. 1437-1454. ISSN 0018-9251
Whiteley, NP and Johansen, AM and Godsill, SJ (2010) Monte Carlo filtering of piecewise deterministic processes. Journal of Computational and Graphical Statistics. pp. 1-21. ISSN 1061-8600
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 ]
Lombardi, MJ and Godsill, SJ (2006) On-line Bayesian estimation of signals in symmetric /spl alpha/-stable noise. IEEE Transactions on Signal Processing, 54. pp. 775-779. ISSN 1053-587X
S. J. Godsill and J. Vermaak, Models and algorithms for tracking using trans-dimensional sequential Monte Carlo. In Proc. IEEE ICASSP 2004.#
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
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.
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.
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.
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.
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.
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.
Doucet, S. J. Godsill, and C. Andrieu. On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing, 10:197-208, 2000.
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:
3F3 - Signal
and Pattern processing
IB Paper 6 - Signal
and Data Analysis
Other links:
Book - Digital Audio Restoration - a statistical model-based approach (Springer-Verlag 1998)
Old list of Publications (including downloadable Postscript/pdf)
Bayesian Picture Gallery
[Cambridge University | CUED |Signal
Processing Group
Updated April 2013