New signal decomposition method based speech enhancement

Signal Processing

Volumn 87, Issue 11, 2007, Pages 2607-2628

  Tantibundhit, C ^a   Boston, J R ^b   Li, C C ^b   Durrant, J D ^c   Shaiman, S ^c   Kovacyk, K ^c   El Jaroudi, A ^b  

^a THAMMASAT UNIVERSITY   (Thailand)

^b PA 15260   (United States)

^c UNIVERSITY OF PITTSBURGH   (United States)

Author keywords

Confusion matrix; Mixture of two univariate Gaussian distributions; Modified speech; Psychoacoustic test; Residual component; Speech enhancement; Speech intelligibility; Speech perception; The hidden Markov chain (HMC) model; The hidden Markov tree model; The maximum a posteriori (MAP) algorithm; The modified discrete cosine transform (MDCT); The modified rhyme protocol; The Viterbi algorithm; The wavelet transform; Tonal component; Transient component

Indexed keywords

AUDITORY SYSTEM; BACKGROUND NOISE; SIGNAL DECOMPOSITION METHOD; SPEECH PERCEPTION;

ADAPTIVE ALGORITHMS; DISCRETE COSINE TRANSFORMS; HIDDEN MARKOV MODELS; SIGNAL PROCESSING; SIGNAL TO NOISE RATIO; SPEECH INTELLIGIBILITY; SPEECH RECOGNITION;

SPEECH ENHANCEMENT;

EID: 34447272226     PISSN: 01651684     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.sigpro.2007.04.014     Document Type: Article

References (31)

1. Lim J.S., and Oppenheim A.V. Enhancement and bandwidth compression of noisy speech. Proc. IEEE 67 12 (1979) 1586-1604
2. Hazen V., and Simpson A. The effect of cue-enhancement on the intelligibility of nonsense word and sentence materials presented in noise. Speech Commun. 24 12 (1998) 211-226
3. S. Yoo, Speech decomposition and speech enhancement, Ph.D. thesis, Department of Electrical and Computer Engineering, University of Pittsburgh, 2005.
4. Niederjohn R.J., and Grotelueschen J.H. The enhancement of speech intelligibility in high noise levels by high-pass filtering followed by rapid amplitude compression. IEEE Trans. Acoust. Speech Signal Process. 24 4 (1976) 202-207
5. Daudet L., and Torrésani B. Hybrid representation for audiophonic signal encoding. Signal Processing 82 11 (2002) 1595-1617
6. Crouse M.S., Nowak R.D., and Baraniuk R.G. Wavelet-based statistical signal processing using hidden Markov models. IEEE Trans. Signal Process. 46 4 (1998) 886-902
7. Daudet L., Molla S., and Torrésani B. Towards a hybrid audio coder. In: Li J.P. (Ed). Proceedings of the International Conference Wavelet Analysis and Applications, Chongqing, China (2004), World Scientific, Singapore 12-21
8. Donoho D., and Johnstone I. Adapting to unknown smoothness via wavelet shrinkage. Internat. Amer. Statist. Assoc. 90 (1995) 1200-1224
9. Chipman H., Kolaczyk E., and McCulloch R. Adaptive Bayesian wavelet shrinkage. Internat. Amer. Statist. Assoc. 92 (1997) 1413-1421
10. S. Molla, B. Torrésani, Hidden Markov tree of wavelet coefficients for transient detection in audiophonic signals, in: Proceedings of the Conference Self-Similarity and Applications, Clermont-Ferrand, 2002.
11. J.B. Durand, P. Gonçalvès, Statistical inference for hidden Markov tree models and application to wavelet trees, Technical Report 4248, Institut National de Recherche en Informatique et en Automatique, September 2001.
12. Rabiner L.R. A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77 2 (1989) 257-286
13. Tantibundhit C., Boston J.R., Li C.C., and El-Jaroudi A. Automatic speech decomposition and speech coding using MDCT-based hidden Markov chain and wavelet-based hidden Markov tree models. Proceedings of IEEE Workshop on Application of Signal Processing to Audio and Acoustics. New Paltz, NY (2005) 207-210
14. Tantibundhit C., Boston J.R., Li C.C., Durrant J.D., Kovacyk K., Shaiman S., and El-Jaroudi A. Speech enhancement using transient speech components. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing. Toulouse, France (2006) 833-836
15. Princen J.P., and Bradley A.B. Analysis/synthesis filter bank design based on time domain aliasing cancellation. IEEE Trans. Acoust. Speech Signal Process. 34 5 (1986) 1153-1161
16. Mallat S. A Wavelet Tour of Signal Processing (1998), Academic Press, San Diego, CA, USA
17. Painter T. Perceptual coding of digital audio. Proc. IEEE 88 4 (2000) 451-513
18. Malvar H. Lapped transforms for efficient transform/subband coding. IEEE Trans. Acoust. Speech Signal Process. 38 6 (1990) 969-978
19. C. Tantibundhit, Speech enhancement using transient speech components, Ph.D. thesis, Department of Electrical and Computer Engineering, University of Pittsburgh, 2006.
20. T. Tillman, R. Carhart, An expanded test for speech discrimination utilizing CNC monosyllabic words, Technical Report SAM-TR-66-55, Northwestern University Auditory Test No. 6, 1966.
21. House A.S., Williams C.E., Hecker H.M.L., and Kryter K.D. Articulation-testing methods: consonantal differentiation with a closed-response set. J. Acoust. Soc. Amer. 37 1 (1965) 158-166
22. Vlassis N., and Likas A. A greedy EM algorithm for Gaussian mixture learning. Neural Processing Lett. 15 1 (2002) 77-87
23. Dempster A.P., Laird N.M., and Rubin D.B. Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Statist. Soc. B 39 1 (1977) 1-38
24. Daubechies I. Ten Lectures on Wavelets (1992), SIAM, Philadelphia, PA, USA
25. Horgan G.W. Using wavelets for data smoothing: a simulation study. J. Appl. Statist. 26 8 (1999) 923-932
26. Berger J., Coifman R., and Goldberg M. Removing noise from music using local trigonometric bases and wavelet packets. J. Audio Eng. Soc. 42 10 (1994) 808-818
27. Daudet L., and Sandler M. MDCT analysis of sinusoids: exact results and applications to coding artifacts reduction. IEEE Trans. on Speech and Audio Process. 12 3 (2004) 302-312
28. Mackersie C., Neuman A.C., and Levitt H. A comparison of response time and word recognition measures using a word-monitoring and closed-set identification task. Ear and Hearing 20 2 (1999) 140-148
29. French N.R., and Steinberg J.C. Factors governing the intelligibility of speech sounds. J. Acoust. Soc. Amer. 19 1 (1947) 90-119
30. Miller G.A., and Nicely P.E. An analysis of perceptual confusions among some English consonants. J. Acoust. Soc. Amer. 27 2 (1955) 338-352
31. Fairbanks G. Test of phoenemic differentiation: the rhyme test. J. Acoust. Soc. Amer. 30 7 (1958) 596-600