1. Titze, I. R., Principles of Voice Production, Prentice Hall, 1994.
doi:10.1121/1.424266
2. Li, S., R. C. Scherer, M. Wan, S. Wang, and H. Wu, "The effect of glottal angle on intraglottal pressure," J. Acoust. Soc. Am, Vol. 119, No. 1, 539-548, 2006.
doi:10.1121/1.2133491 Google Scholar
3. Li, S., R. C. Scherer, W. Minxi, S. Wang, and H. Wu, "Numerical study of the effects of inferior and superior vocal fold surface angles on vocal fold pressure distributions," J. Acoust. Soc. Am, Vol. 119, No. 5, 3003-3010, 2006.
doi:10.1121/1.2186548 Google Scholar
4. Yanagisawa, T. and K. Furihata, "Pickup of speech signal utilization of vibration transducer under high ambient noise," J. Acoust. Soc. Jpn., Vol. 31, No. 3, 213-220, 1975. Google Scholar
5. Li, Z.-W., "Millimeter wave radar for detecting the speech signal applications," International Journal of Infrared and Millimeter Waves, Vol. 17, No. 12, 2175-2183, 1996.
doi:10.1007/BF02069493 Google Scholar
6. Li, S., J. Wang, M. Niu, and X. Jing, "The enhancement of millimeter wave conduct speech based on perceptual weighting," Progress In Electromagnetics Research B, Vol. 9, 199-214, 2008.
doi:10.2528/PIERB08063001 Google Scholar
7. Park, J.-I. and K.-T. Kim, "A comparative study on isar imaging algorithms for radar target identification," Progress In Electromagnetics Research, Vol. 108, 155-175, 2010.
doi:10.2528/PIER10071901 Google Scholar
8. Lazaro, A., D. Girbau, and R. Villarino, "Analysis of vital signs monitoring using an ir-UWB radar," Progress In Electromagnetics Research, Vol. 100, 265-284, 2010.
doi:10.2528/PIER09120302 Google Scholar
9. Lee, K.-C., J.-S. Ou, and M.-C. Fang, "Application of svd noise-reduction technique to PCA based radar target recognition," Progress In Electromagnetics Research, Vol. 81, 447-459, 2008.
doi:10.2528/PIER08032101 Google Scholar
10. Byrne, D., M. O'halloran, M. Glavin, and E. Jones, "Data independent radar beamforming algorithms for breast cancer detection," Progress In Electromagnetics Research, Vol. 107, 331-348, 2010.
doi:10.2528/PIER10061001 Google Scholar
11. Conceição, R. C., M. O'halloran, E. Jones, and M. Glavin, "Investigation of classifiers for early-stage breast cancer based on radar target signatures ," Progress In Electromagnetics Research, Vol. 105, 295-311, 2010.
doi:10.2528/PIER10051904 Google Scholar
12. Lazaro, A., D. Girbau, and R. Villarino, "Wavelet-based breast tumor localization technique using a UWB radar," Progress In Electromagnetics Research, Vol. 98, 75-95, 2009.
doi:10.2528/PIER09100705 Google Scholar
13. Hasar, U. C., "Procedure for accurate and stable constitutive parameters extraction of materials at microwave frequencies," Progress In Electromagnetics Research, Vol. 109, 107-121, 2010.
doi:10.2528/PIER10083006 Google Scholar
14. Holzrichter, J. F., G. C. Burnett, and L.~C. Ng, "Speech articulator measurements using low power EM-wave sensors," J. Acoust. Soc. Am, Vol. 103, No. 1, 622-625, 1998.
doi:10.1121/1.421133 Google Scholar
15. Hu, R. and B. Raj, "A robust voice activity detector using an acoustic doppler radar," IEEE Workshop on Automatic Speech Recognition and Understanding, Vol. 27, 319-324, 2005. Google Scholar
16. Quatieri, T. F., K. Brady, D. Messing, and J. P. Campbell, "Exploiting nonacoustic sensors for speech encoding," IEEE Transactions on Audio, Speech and Language Processing, Vol. 14, No. 2, 533-544, 2006.
doi:10.1109/TSA.2005.855838 Google Scholar
17. Jiao, M., G. Lu, X. Jing, S. Li, Y. Li, and J. Wang, "A novel radar sensor for the non-contact detection of speech signals," Sensors, Vol. 10, No. 5, 4622-4633, 2010.
doi:10.3390/s100504622 Google Scholar
18. Bellomo, L., S. Pioch, M. Saillard, and E. Spano, "Time reversal experiments in the microwave range: Description of the radar and results ," Progress In Electromagnetics Research, Vol. 104, 427-448, 2010.
doi:10.2528/PIER10030102 Google Scholar
19. Polivka, J., P. Fiala, and J. Machac, "Microwave noise field behaves like white light," Progress In Electromagnetics Research, Vol. 111, 311-330, 2011.
doi:10.2528/PIER10041304 Google Scholar
20. Guo, B. and G. Wen, "Periodic time-varying noise in current-commutating cmos mìxers," Progress In Electromagnetics Research, Vol. 117, 283-298, 2011. Google Scholar
21. Boll, S. F., "Suppression of acoustic noise in speech using spectral subtraction ," IEEE Transactions on Acoustics Speech and Signal Processing, Vol. 27, No. 2, 113-120, 1979.
doi:10.1109/TASSP.1979.1163209 Google Scholar
22. Berouti, M., R. Schwartz, and J. Makhoul, "Enhancement of speech corrupted by acoustic noise," Proc. IEEE Int. Conf. Acoust., Speech, Signal Process, Vol. 4, 208-211, 1979. Google Scholar
23. Mallat, S., A Wavelet Tour of Signal Processing, A Harcourt Science and Technology, Academic-Press, 1999.
24. Strang, G. and T. Nguyen, Wavelets and Filter Banks, Wellesley-Cambridge Press, 1996.
25. Chong, N. R., I. S. Burnett, and J. F. Chicharo, "A new waveform interpolation coding scheme based on pitch synchronous wavelet transform decomposition," IEEE Trans. Speech Audio Process, Vol. 8, No. 3, 345-348, 2000.
doi:10.1109/89.841216 Google Scholar
26. Srinivasan, P. and L. Jamieson, "High-quality audio compress using an adaptive wavelet packet decomposition and psychoacoustic modeling," IEEE Trans. Signal Procession, Vol. 46, 1085-1093, 1998.
doi:10.1109/78.668558 Google Scholar
27. Deng, H. and H. Ling, "Clutter reduction for synthetic aperture radar imagery based on adaptive wavelet packet transform," Progress In Electromagnetics Research, Vol. 29, 1-23, 2000.
doi:10.2528/PIER99120602 Google Scholar
28. Alyt, O. A. M., A. S. Omar, and A. Z. Elsherbeni, "Detection and localization of RF radar pulses in noise environments using wavelet packet transform and higher order statistics," Progress In Electromagnetics Research, Vol. 58, 301-317, 2006.
doi:10.2528/PIER05070204 Google Scholar
29. Hatamzadeh-Varmazyar, S., M. Naser-Moghadasi, E. Babolian, and Z. Masouri, "Calculating the radar cross section of the resistive targets using the haar wavelets," Progress In Electromagnetics Research, Vol. 83, 55-80, 2008.
doi:10.2528/PIER08042504 Google Scholar
30. Tsai, H.-C., "Investigation into time- and frequency-domain emi-induced noise in bistable multivibrator," Progress In Electromagnetics Research, Vol. 100, 327-349, 2010.
doi:10.2528/PIER09112904 Google Scholar
31. Dl, D. and Denoising by soft thresholding, "IEEE Trans. Inform Theory,", Vol. 41, No. 3, 613-627, 1995. Google Scholar
32. Fu, Q. and E. Wan, "Perceptual wavelet adaptive denoising of speech," Eurospeech, 578-580, 2003. Google Scholar
33. Ayat, S., M. T. Manzuri-Shalmani, and R. Dianat, "An improved wavelet-based speech enhancement by using speech signal features," Computers and Electrical Engineering, Vol. 32, 411-425, 2006.
doi:10.1016/j.compeleceng.2006.05.002 Google Scholar
34. Sheikhzadeh, H. and H. Abutalebi, "An improved wavelet-based speech enhancement system," Eurospeech, 1855-1858, 2001. Google Scholar
35. Mahmoudi, D., "A microphone array for speech enhancement using multiresolution wavelet transform," Proc. of Eurospeech, 339-342, Rhodes, Greece, 1997. Google Scholar
36. Gulzow, T., A. Engelsberg, and U. Heute, "Comparison of a discrete wavelet transformation and nonuniform polyphase ¯lterbank applied to spectral-subtraction speech enhancement," Signal Processing, Vol. 64, 5-19, 1998.
doi:10.1016/S0165-1684(97)00172-2 Google Scholar
37. Sika, J. and V. Davidek, "Multi-channel noise reduction using wavelet filter bank," Eurospeech, 2595-2598, Rhodes, Greece, 1997. Google Scholar
38. Bahoura, M. and J. Rouat, "Wavelet speech enhancement based on time-scale adaptation," Speech Communication, Vol. 48, 1620-1637, 2006.
doi:10.1016/j.specom.2006.06.004 Google Scholar
39. Gray, R. M., Entropy and Information Theory, Springer, 1990.
40. Wang, J., C. Zheng, X. Jin, G. Lu, H. Wang, and A. Ni, "Study on a non-contact life parameter detection system using millimeter wave ," Space Medicine & Medical Engineering, Vol. 17, No. 3, 157-161, 2004. Google Scholar
41. Udrea, R. M., S. Ciochina, and D. N. Vizireanu, "Multi-band bark scale spectral over-subtraction for colored noise reduction," International Symposium on Signals, Circuits and Systems, Vol. 1, 311-314, 2005.
doi:10.1109/ISSCS.2005.1509916 Google Scholar
42. Ghanbari, Y., M. Reza, and M. R. Karami-Mollaei, "A new approach for speech enhancement based on the adaptive thresholding of the wavelet packets ," Speech Communication, Vol. 48, 927-940, 2006.
doi:10.1016/j.specom.2005.12.002 Google Scholar
43. Blanco, S., A. Figliola, R. Q. Quiroga, O. A. Rosso, and E. Serrano, "Time-frequency analysis of electroencephalogram series. III. Wavelet packets and information cost function," Phys. Rev., Vol. 57, No. 1, 932-940, 1998. Google Scholar
44. Shannon, C. E., "A mathematical theory of communication," Bell System Technical Journal, Vol. 27, Nos. 379-423 and 623-656, 1948. Google Scholar
45. Rosso, O. A., S. Blanco, J. Yordanova, V. Kolev, A. Figliola, M. Schürmann, and E. Basar, "Wavelet entropy: A new tool for analysis of short duration brain electrical signals," Journal of Neuroscience Methods, Vol. 105, No. 1, 65-75, 2001.
doi:10.1016/S0165-0270(00)00356-3 Google Scholar
46. Bahoura, M. and J. Rouat, "Wavelet speech enhancement based on the teager energy operator," IEEE Signal Process. Lett., Vol. 8, 10-12, 2001.
doi:10.1109/97.889636 Google Scholar
47. Johnstone, I. and B. Silverman, "Wavelet threshold estimators for data with correlated noise," J. Roy. Statist. Soc., Vol. 59, No. 2, 319-351, 1997.
doi:10.1111/1467-9868.00071 Google Scholar
48. Donoho, D. and I. Johnstone, "Ideal spatial adaptation by wavelet shrinkage," Biometrika, Vol. 81, 425-455, 1994.
doi:10.1093/biomet/81.3.425 Google Scholar
49. Donoho, D. and I. Johnstone, "Adapting to unknown smoothness via wavelet shrinkage," J. Amer. Stat. Assoc., Vol. 90, No. 432, 1200-1224, 1995.
doi:10.1080/01621459.1995.10476626 Google Scholar
50. Rangachari, S. and P. C. Loizou, "A noise-estimation algorithm for highly non-stationary environments," Speech Communication, Vol. 48, 220-231, 2006.
doi:10.1016/j.specom.2005.08.005 Google Scholar