Online bayesian inference in some time-frequency representations of non-stationary processes

IEEE Transactions on Signal Processing

Volumn 61, Issue 22, 2013, Pages 5755-5766

  Everitt, Richard Geoffrey ^a   Andrieu, Christophe ^b   Davy, Manuel ^c  

^a UNIVERSITY OF READING   (United Kingdom)

^b UNIVERSITY OF BRISTOL   (United Kingdom)

^c CNRS   (France)

Author keywords

Bayesian methods; DSP TFSR; Frequency domain analysis EDICS Categories; MLR BAYL; MLR MUSI; Particle filters; Signal processing algorithms; Spectrogram; SSP NSSP; SSPTRAC

Indexed keywords

BAYESIAN METHODS; DSP-TFSR; MLR-BAYL; MLR-MUSI; PARTICLE FILTER; SIGNAL PROCESSING ALGORITHMS; SPECTROGRAMS; SSP-NSSP; SSPTRAC;

ALGORITHMS; DISTRIBUTED COMPUTER SYSTEMS; ESTIMATION; FREQUENCY DOMAIN ANALYSIS; INFERENCE ENGINES; MONTE CARLO METHODS; SIGNAL FILTERING AND PREDICTION; SPECTRAL DENSITY; TARGET TRACKING;

BAYESIAN NETWORKS;

EID: 84886649776     PISSN: 1053587X     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSP.2013.2280128     Document Type: Article

References (40)

1. W. J. Pielemeier, G. H.Wakefield, andM. H. Simoni, "Time-frequency analysis ofmusical signals," Proc. IEEE, vol. 84, no. 9, pp. 1216-1230, Sep. 1996.
2. J. Dripps, "An introduction to time-frequency methods applied to biomedical signals," in Proc. Time-Frequency Anal. Biomed. Signals (Digest/006), IEE Colloquium IET, 1997, p. 1.
3. I. Spulber, P. Georgiou, A. Eftekhar, C. Toumazou, L. Duffell, J. Bergmann, A. McGregor, T. Mehta, M. Hernandez, and A. Burdett, "Frequency analysis of wireless accelerometer and EMG sensors data: Towards discrimination of normal and asymmetric walking pattern," in Proc. 2012 IEEE Int. Symp. Circuits Syst. (ISCAS), 2012, pp. 2645-2648.
4. P. Flandrin, "A time-frequency formulation of optimum detection," IEEE Trans. Acoust., Speech, Signal Process., vol. 36, no. 9, pp. 1377-1384, Sep. 1988.
5. M. Davy, A. Gretton, A. Doucet, and P. J. W. Rayner, "Optimized support vector machines for nonstationary signal classification," IEEE Signal Process. Lett., vol. 9, no. 12, pp. 442-445, Sep. 2002.
6. C. Richard, "Time-frequency-based detection using discrete-time discrete- frequency Wigner distributions," IEEE Trans. Signal Process., vol. 50, no. 9, pp. 2170-2176, Sep. 2002. (Pubitemid 34974209)
7. B. W. Gillespie and L. E. Atlas, "Optimizing time-frequency kernels for classification," IEEE Trans. Signal Process., vol. 49, no. 3, pp. 485-496, Mar. 2001. (Pubitemid 32583151)
8. E. Chassande-Mottin, P. Flandrin, and F. Auger, "On the statistics of spectrogram reassignment vectors," Multidimensional Syst. Signal Process., vol. 9, no. 4, 1998.
9. S. Barbarossa, "Analysis of multicomponent LFM signals by a combinedWigner-Hough transform," IEEE Trans. Signal Process., vol. 43, no. 6, pp. 1511-1515, Jun. 1995.
10. B. Boashash, "Estimating and interpreting the instantaneous frequency of a signal," Proc. IEEE, vol. 80, no. 4, pp. 540-568, Apr. 1992.
11. O. Michel, P. Flandrin, and A. O. Hero III, "Automatic extraction of time-frequency skeletons with minimal spanning trees," in Proc. of the IEEE Int. Conf. on Acoust., Speech, and Signal Processing, 2000.
12. M. Davy and C. Doncarli, "A new nonstationary test procedure for improved loudspeaker fault detection," J. Audio Eng. Soc., vol. 50, no. 6, 2002.
13. P.Bonato, S. H. Roy, M. Knaflitz, andC. J.DeLuca, "Time-Frequency Parameters of the SurfaceMyoelectric Signal for Assessing Muscle Fatigue during Cyclic Dynamic Contractions," IEEE Trans. Biomedical Eng., vol. 48, no. 7, pp. 745-753, Jul. 2001. (Pubitemid 32574934)
14. F. Hlawatsch and F. Auger, Time-frequency analysis: Concepts and methods. Hoboken, NJ, USA: Wiley, 2008.
15. P. J. Wolfe, S. J. Godsill, and W.-J. Ng, "Bayesian variable selection and regularization for time-frequency surface estimation," J. Roy. Statist. Soc. Series B, vol. 66, no. 3, pp. 575-589, 2004. (Pubitemid 39015902)
16. M. Davy, S. J. Godsill, and J. Idier, "Bayesian analysis of polyphonic western tonal music," J. Acoust. Soc. Amer., vol. 119, no. 4, pp. 2498-2517, 2006.
17. C. Andrieu, M. Davy, and A. Doucet, "Efficient particle filtering for jump Markov systems. Application to time-varying autoregressions," IEEE Trans. Signal Process., vol. 51, no. 7, pp. 1762-1769, Jul. 2003.
18. C. Dubois, M. Davy, and J. Idier, "Tracking of time-frequency components using particle filtering," in Proc. Int. Conf. Acoust., Speech, Signal Process., 2005.
19. O. Rosen, D. S. Stoffer, and S. Wood, "Local spectral analysis via a Bayesian mixture of smoothing splines," J. Amer. Statist. Assoc., vol. 104, no. 485, pp. 249-262, 2009.
20. S. Wood, O. Rosen, and D. Stoffer, Adaptspec: Adaptive spectral estimation for nonstationary time series. Pittsburgh, PA, USA: Univ. Pittsburgh, Tech. Rep, 2011.
21. R. Dahlhaus, "A likelihood approximation for locally stationary processes," Annal. Statist., vol. 28, pp. 1762-1794, 2000. (Pubitemid 33274483)
22. P. Whittle, "The analysis of multiple stationary time series," J. Roy. Statist. Soc., Series B, vol. 15, no. 1, pp. 125-139, 1953.
23. K. Dzhaparidze, Parameter Estimation and Hypothesis Testing in Spectral Analysis of Stationary Time Series. Berlin, Germany: Springer-Verlag, 1986.
24. C. Carter and R. Kohn, "Semiparametric Bayesian inference for time series with mixed spectra," J. Roy. Statist. Soc., Series B, vol. 59, no. 1, pp. 255-268, 1997. (Pubitemid 127686952)
25. G. Petris and M.West, Bayesian Time Series Modelling and Rediction with Long Range Dependence 1998, Duke Univ., Tech. Rep.
26. N. Choudhuri, S.Ghosal, andA. Roy, "Bayesian estimation of the spectral density of a time series," J. Amer. Statist. Assoc., vol. 99, no. 468, pp. 1050-1059, 2004. (Pubitemid 39665950)
27. R. Dahlhaus, "On the kullback-leibler information divergence of locally stationary processes," Stochastic Process. Appl., vol. 62, pp. 139-168, 1996. (Pubitemid 126161994)
28. R. Dahlhaus, Asymptotic Statistical Inference for Nonstationary Processes with Evolutionary Spectra Universität Heidelberg, Tech. Rep, 1996.
29. M. Neumann and R. von Sachs, "Wavelet thresholding in anisotropic function classes and application to adaptive estimation of evolutionary spectra," Annal. Statist., vol. 25, pp. 38-76, 1997.
30. R. Dahlhaus, "Fitting time series models to non-stationary processes," Ann. of Statistics, vol. 24, pp. 1-37, 1997.
31. A. Doucet, N. De Freitas, and N. Gordon, Sequential Monte Carlo in Practice. Berlin, Germany: Springer-Verlag, 2001.
32. A. Doucet and A. M. Johansen, "A tutorial on particle filtering and smoothing: Fifteen years later," in Oxford Handbook of Nonlinear Filtering, D. Crisan and B. Rozovsky, Eds. London, U.K.: Oxford Univ. Press, 2009.
33. H. R. Künsch, "Statistical aspects of self-similar processes," in Proceedings of the First World Congress of the Bernoulli Society, Y. Prohorov and V. Sazanov, Eds. Utrecht, The Netherlands: VNU Science Press, 1987, vol. 1, pp. 67-74.
34. M. Priestley, Spectral Anal. Time Series. New York, NY, USA: Academic Press, 1981.
35. N. J. Gordon, D. J. Salmond, and A. F. M. Smith, "Novel approach to nonlinear/non-Gaussian Bayesian state estimation," IEE Proc. Radar Signal Process., vol. 140, no. 2, pp. 107-113, 1993.
36. A. Klapuri, "Pitch estimation using multiple independent time-frequency windows," in Proc. 1999 IEEEWorkshop Appl. Signal Process. Audio Acoust., 1999, pp. 115-118. (Pubitemid 32214030)
37. A. Klapuri and M. Davy, Signal Processing Methods for Music Transcription. Berlin, Germany: Springer-Verlag, 2006.
38. M. Davy and S. J. Godsill, "Musical signal analysis," in Bayesian Statistics 7, J.M. Bernardo, M.J. Bayarri, J.O Berger, A.P. Dawid, D. Heckerman, A.F.M. Smith, and M. West, Eds. London, U.K.: Oxford Univ. Press, 2003, pp. 105-124.
39. C. Dubois and M. Davy, "Joint detection and tracking of time-varying harmonic components: a flexible Bayesian approach," IEEE Trans. Audio, Speech, Lang. Process., vol. 15, no. 4, pp. 1283-1295, May 2008.
40. P. Flandrin, "Time-frequency/time-scale analysis," in Translated from the French by Joachim Stöckler. New York, NY, USA: Academic Press, 1999.