Filtering of Colored Noise for Speech Enhancement and Coding

IEEE Transactions on Signal Processing

Volumn 39, Issue 8, 1991, Pages 1732-1742

  Gibson, Jerry D ^a   Koo, Boneung ^b   Gray, Steven D ^c  

^a TEXAS A AND M UNIVERSITY   (United States)

^b Kyonggi University   (South Korea)

^c MITRE CORPORATION   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MATHEMATICAL TECHNIQUES - STATE SPACE METHODS; NOISE, SPURIOUS SIGNAL - SIGNAL TO NOISE RATIO; PROBABILITY; SIGNAL FILTERING AND PREDICTION - KALMAN FILTERING;

COLORED NOISE FILTERING; KALMAN FILTERS; SPEECH CODING; SPEECH ENHANCEMENT; WHITE NOISE FILTERING; WIENER FILTERS;

SPEECH;

EID: 0026205299     PISSN: 1053587X     EISSN: 19410476     Source Type: Journal    
DOI: 10.1109/78.91144     Document Type: Article

References (37)

1. M. R. Sambur and N. S. Jayant, “LPC analysis/synthesis from speech inputs containing quantization noise or additive white noise,” IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-24, pp. 488-494, Dec. 1976.
2. C. F. Teacher and D. Coulter, “Performance of LPC vocoders in a noisy environment,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing (Washington, DC), Apr. 2-4, 1979, pp. 216-219.
3. J. D. Gibson, “Theory of LPC analysis for distorted inputs,” in Proc. Eighteenth Annu. Allerton Conf. Commun., Contr., Computing, (Monticello, IL), Oct. 8-10, 1980, p. 955.
4. J. S. Lim and A. V. Oppenheim, “All-pole modeling of degraded speech,” IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-26, pp. 197-210, June 1978.
5. K. K. Paliwal and A. Basu, “A speech enhancement method based on Kaiman filtering, “ in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing (Dallas, TX), Apr. 6-9, 1987, pp. 177-180.
6. J. K. Wolf and J. Ziv, “Transmission of noisy information to a noisy receiver with minimum distortion,” IEEE Trans. Inform. Theory, vol. IT-16, pp. 406-411, July 1970.
7. H. S. Witsenhausen, “Indirect rate distortion problems,” IEEE Trans. Inform. Theory, vol. IT-26, pp. 518-521, Sept. 1980.
8. J. D. Gibson and T. R. Fischer, “Alphabet-constrained data compression,” IEEE Trans. Inform. Theory, vol. IT-28, pp. 443-457, May 1982.
9. K. Kroschel, “A comparison of quantizers optimized for corrupted and uncorrupted input signals,” Signal Processing, vol. 12, pp. 169-176, 1987.
10. E. Ayanoglu, “Optimal quantization of noisy sources,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, (New York, NY), Apr. 11-14, 1988, pp. 565-568.
11. J. D. Gibson, T. R. Fischer, and B. Koo, “Estimation and vector quantization of noisy speech,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing (New York, NY), Apr. 11-14, 1988, pp. 541-544.
12. Y. Ephraim and R. M. Gray, “A unified approach for encoding clean and noisy sources by means of waveform and autoregressive model vector quantization,” IEEE Trans. Inform. Theory, vol. 34, pp. 826-834, July 1988.
13. B. R. Musicus, “Iterative algorithms for optimal signal reconstruction and parameter identification given noisy and incomplete data,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing (Boston, MA), Apr. 14-16, 1983, pp. 235-238.
14. A. Dembo, “Signal reconstruction from noisy partial information of its transform,” IEEE Trans. Acoust., Speech, Signal Processing, vol. 37, pp. 65-72, Jan. 1989.
15. A. P. Dempster, N. M. Laird, and D. B. Rubin, “Maximum likelihood from incomplete data via the EM algorithm,” J. Roy. Stat. Soc, ser. B, vol. 39, pp. 1-39, 1977.
16. J. H. L. Hansen and M. A. Clements, “Constrained iterative speech enhancement with application to speech recognition,” IEEE Trans. Signal Processing, vol. 39, no. 4, pp. 795-805, Apr. 1991.
17. A. P. Sage and J. L. Melsa, Estimation Theory with Applications to Communications and Control. New York: McGraw-Hill, 1971.
18. P. S. Maybeck, Stochastic Models, Estimation, and Control, vol. 1. New York: Academic, 1979.
19. B. D. O. Anderson and J. B. Moore, Optimal Filtering. Englewood Cliffs, NJ: Prentice-Hall, 1979.
20. M. Pagano, “Estimation of models of autoregressive signals plus white noise,” Ann. Stat., vol. 2, pp. 99-108, 1974.
21. B. Friedlander and B. Porat, “A spectral matching technique for ARMA parameter estimation,” IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-32, pp. 338-343, Apr. 1984.
22. W. J. Done and C.K. Rushforth, “Estimating the parameters of a noisy all-pole process using pole-zero modeling,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing (Washington, DC), Apr. 2-4, 1979, pp. 228-231.
23. K. Bry and J. LeRoux, “Comparison of some algorithms for identifying autoregressive signals in the presence of noise,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing (Paris, France), May 3-5, 1982, pp. 224-227.
24. K. K. Paliwal, “A noise-compensated long correlation matching method for AR spectral estimation of noisy signals,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing (Tokyo, Japan), Apr. 8-11, 1986, pp. 1369-1372.
25. M. Feder and E. Weinstein, “Parameter estimation of superimposed signals using the EM algorithm,” IEEE Trans. Acoust., Speech, Signal Processing, vol. 36, pp. 477-489, Apr. 1988.
26. C. R. Rao, Linear Statistical Inference and Its Applications. New York: Wiley, 1965.
27. C. F. J. Wu, “On the convergence properties of the EM algorithm,” Ann. Stat., vol. 11, pp. 95-103, 1983.
28. L. R. Rabiner and R. W. Schafer, Digital Processing of Speech Signals. Englewood Cliffs, NJ: Prentice-Hall, 1978.
29. F. Itakura, “Minimum prediction residual principle applied to speech recognition,” IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-23, pp. 67-72, Feb. 1975.
30. P. de Souza and P. J. Thomson, “LPC distance measures and statistical tests with particular reference to the likelihood ratio,” IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-30, pp. 304-315, Apr. 1982.
31. B.-H. Juang, “On using the Itakura-Saito measures for speech coder performance evaluation,” AT&T Bell Lab. Tech. J., vol. 63, pp. 1477-1498, Oct. 1984.
32. D. E. Knuth, The Art of Computer Programming, vol. 2, 2nd ed. Reading, MA: Addison-Wesley, 1981.
33. B. Friedlander and B. Porat, “A spectral matching technique for ARMA parameter estimation,” IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-32, pp. 338-343, Apr. 1984.
34. Y. Linde, A. Buzo, and R. M. Gray, “An algorithm for vector quantizer design,” IEEE Trans. Commun., vol. COM-28, pp. 84-95, 1980.
35. B. R. Musicus, “An iterative technique for maximum likelihood estimation with noisy data,” M. S. thesis, Dep. Elec. Eng. Comput. Sci., M.I.T., Cambridge, MA, Feb. 20, 1979.
36. B. R. Musicus, “Iterative algorithms for optimal signal reconstruction and parameter identification given noisy and incomplete data,” Ph.D. dissertation, Dep. Elec. Eng., M.I.T., Cambridge, MA, Aug. 1982.
37. B. Koo, “Structures for coding noisy speech at medium-to-low bit rates,” Ph.D. dissertation, Dep. Elec. Eng., Texas A&M Univ., Dec. 1988.