Rayleigh mixture model-based hidden Markov modeling and estimation of noise in noisy speech signals

IEEE Transactions on Audio, Speech and Language Processing

Volumn 15, Issue 3, 2007, Pages 901-917

  Sørensen, Karsten Vandborg ^a,b   Andersen, Søren Vang ^a  

^a AALBORG UNIVERSITY   (Denmark)

^b Sonorit ApS   (Denmark)

Author keywords

Hidden Markov model (HMM); Probability density function; Rayleigh mixture model (RMM); Speech enhancement

Indexed keywords

EXPECTATION MAXI- MIZATION; GAUSSIAN MIXTURE MODELS; HIDDEN MARKOV MODEL (HMM); HIDDEN MARKOV MODELING; MINIMUM MEAN-SQUARE ERRORS; NEW MODELS; NOISY SPEECH SIGNALS; NUMBER OF COMPONENTS; PERIODOGRAM; PERIODOGRAMS; RAYLEIGH MIXTURE MODEL (RMM); STATIONARY NOISE; STATISTICAL MODELS; TRAINING ALGORITHMS;

BLIND SOURCE SEPARATION; HIDDEN MARKOV MODELS; MIXTURES; OBJECT RECOGNITION; SPEECH ENHANCEMENT;

PROBABILITY DENSITY FUNCTION;

EID: 64149128862     PISSN: 15587916     EISSN: None     Source Type: Journal    
DOI: 10.1109/TASL.2006.885240     Document Type: Article

References (36)

1. K. V. Sørensen and S. V. Andersen, "Hidden Markov modeling of noise periodograms using Rayleigh mixture models," in Proc. Asilomar Conf. Signals, Syst., Comput., 2005, pp. 1666-1670.
2. R. Martin, "Noise power spectral density estimation based on optimal smoothing and minimum statistics," IEEE Trans. Speech Audio Process., vol. 9, no. 4, pp. 504-512, Jul. 2001.
3. D. Brillinger, Time Series: Data Analysis and Theory. San Francisco, CA: Holden-Day, 1981.
4. L. Deng, J. Droppo, and A. Acero, "Estimating cepstrum of speech under the presence of noise using joint prior of static and dynamic features," IEEE Trans. Speech Audio Process., vol. 12, no. 3, pp. 218-233, May 2004.
5. -, "Enhancement of log mel power spectra of speech using a phasesensitive model of the acoustic environment and sequential estimation of the corrupting noise," IEEE Trans. Speech Audio Process., vol. 12, no. 2, pp. 133-143, Mar. 2004.
6. H. W. Sorenson and D. L. Aspach, "Recursive Bayesian estimation using Gaussian sums," Automatica, vol. 7, pp. 465-479, 1971.
7. L. Rabiner and B. H. Juang, Fundamentals of Speech Recognition. Englewood Cliffs, NJ: Prentice-Hall, 1993.
8. G. H. Golub and C. F. Van Loan, Matrix Computations. Baltimore, MD: Johns Hopkins Univ. Press, 1989.
9. P. Hedelin and J. Skoglund, "Vector quantization based on Gaussian mixture models," IEEE Trans. Speech Audio Process., vol. 8, no. 4, pp. 385-401, Jul. 2000.
10. J. Lindblom and J. Samuelsson, "Bounded support Gaussian mixture modeling of speech spectra," IEEE Trans. Speech Audio Process., vol. 11, no. 1, pp. 88-99, Jan. 2003.
11. T. C. Gallaudet and C. P. de Moustier, "High-frequency volume and boundary acoustic backscatter fluctuations in shallowwater," J. Acoust. Soc. Amer., vol. 114, no. 2, pp. 707-725, 2003.
12. J. Sun, D. Gu, S. Zhang, and Y. Chen, "Hidden Markov Bayesian texture segmentation using complex wavelet transform," in Proc. IEE Vision, Image, Signal Process., 2004, vol. 151, pp. 215-223.
13. R. K. Mallik, "On multivariate Rayleigh and exponential distributions," IEEE Trans. Inf. Theory, vol. 49, no. 6, pp. 1499-1515, Jun. 2003.
14. H. Sameti, H. Sheikhzadeh, L. Deng, and R. L. Brennan, "HMM-based strategies for enhancement of speech signals embedded in nonstationary noise," IEEE Trans. Speech Audio Process., vol. 6, no. 5, pp. 445-455, Sep. 1998.
15. P. Gaunard, C. G. Mubikangiey, C. Couvreur, and V. Fontaine, "Automatic classification of environmental noise events by hidden Markov models," in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., May 1998, vol. 6, pp. 3609-3612.
16. M. H. Ghoreishi and H. Sheikhzadeh, "A hybrid speech enhancement system based on HMM and spectral subtraction," in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., Jun. 2000, vol. 3, pp. 1855-1858.
17. M. Hosoki, T. Nagai, and A. Kurematsu, "Speech signal band width extension and noise removal using subband HMM," in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process.,May 2002, vol. 1, pp. 245-248.
18. V. Krishnamurthy and J. B. Moore, "On-line estimation of hidden Markov model parameters based on the Kullback-Leibler information measure," IEEE Trans. Signal Process., vol. 41, no. 8, pp. 2557-2573, Aug. 1993.
19. I. B. Collings, V. Krishnamurthy, and J. B. Moore, "On-line identification of hidden Markov models via recursive prediction error techniques," IEEE Trans. Signal Process., vol. 42, no. 12, pp. 3535-3539, Dec. 1994.
20. K. V. Sørensen and S. V. Andersen, "Speech enhancement with natural sounding residual noise based on connected time-frequency speech presence regions," EURASIP J. Appl. Signal Process., vol. 2005, no. 18, pp. 2954-2964, Oct. 2005.
21. -, "Noise power spectral density estimation from noisy speech using online trained hidden Markov models," in Proc. EURASIP Eur. Signal Process. Conf., Sep. 2005.
22. K. S. Shanmugan and A. M. Breipohl, Random Signals - Detection, Estimation and Data Analysis. New York: Wiley, 1988.
23. R. Martin and T. Lotter, "Optimal recursive smoothing of non-stationary periodograms," in Proc. IEEE Int. Workshop Acoust. Echo Noise Control, Sep. 2001, pp. 43-46.
24. L. Råde and B. Westergren, Mathematics Handbook, 4th ed. Lund, Sweden: Studentlitteratur, 1998.
25. D. A. Reynolds and R. C. Rose, "Robust text-independent speaker identification using Gaussian mixture speaker models," IEEE Trans. Speech Audio Process., vol. 3, no. 1, pp. 72-83, Jan. 1995.
26. T. K. Moon, "The expectation-maximization algorithm," IEEE Signal Process. Mag., vol. 13, no. 6, pp. 47-60, Nov. 1996.
27. L. E. Baum and J. A. Eagon, "An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for ecology," Bull. Amer. Math. Soc., vol. 73, pp. 360-363, 1967.
28. L. E. Baum, T. Petrie, G. Soules, and N.Weiss, "A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains," Ann. Math. Statistics, vol. 41, pp. 164-171, 1970.
29. X. Huang, A. Acero, and H. W. Hon, Spoken Language Processing. Upper Saddle River, NJ: Prentice-Hall, 2001.
30. S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge, U.K.: Cambridge Univ. Press, 2004.
31. C. A. Rødbro, M. N. Murthi, S. V. Andersen, and S. H. Jensen, "Hidden Markov model-based framework for packet loss concealment for voice over IP," IEEE Trans. Speech and Audio Process., vol. 14, no. 5, pp. 1609-1623, Sep. 2006.
32. E. Seneta, Non-Negative Matrices and Markov Chains. New York: Springer-Verlag, 1981.
33. P. J. Davis, Circulant Matrices. NewYork: Chelsea Publishing, 1979.
34. quot;Real-life environment sound examples,"Widex, Vaerloese, Denmark.
35. Sounddogs.com, Inc., Online Sound Effects Library [Online]. Available: http://www.sounddogs.com/
36. J. R. Deller Jr., J. H. L. Hansen, and J. G. Proakis, Discrete-Time Processing of Speech Signals. New York: Wiley-Interscience, 2000.