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Progress In Electromagnetics Research
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A NEW KIND OF NON-ACOUSTIC SPEECH ACQUISITION METHOD BASED ON MILLIMETER WAVERADAR

By S. Li, Y. Tian, G. Lu, Y. Zhang, H. J. Xue, J.-Q. Wang, and X.-J. Jing

Full Article PDF (710 KB)

Abstract:
Air is not the only medium that can spread and can be used to detect speech. In our previous paper, another valuable medium - millimeter wave (MMW) was introduced to develop a new kind of speech acquisition technique [Li et al., Progress In Electromagnetics Research B, 9, 199-214, 2008]. Because of the special features of the MMW radar, this speech acquisition method may provide some exciting possibilities for a wide range of applications. In the proposed study, we have designed a new kind of speech acquisition radar system. The super-heterodyne receiver was used in the new system so that to mitigate the severe DC offset problem and the associated 1/f noise at baseband. Furthermore, in order to decrease the harmonic noise, electro-circuit noise, and ambient noise which were combined in the MMW detected speech, an adaptive wavelet packet entropy algorithm is also proposed in this study, which incorporates the wavelet packet entropy based voice/unvoiced radar speech adaptive detection method and the human ear perception properties in a wavelet packet time-scale adaptation speech enhancement process. The performance of the proposed method is evaluated objectively by signal-to-noise ratio and subjectively by mean-opinion-score. The results confirm that the proposed method offers improved effects over other traditional speech enhancement methods for MMW radar speech.

Citation:
S. Li, Y. Tian, G. Lu, Y. Zhang, H. J. Xue, J.-Q. Wang, and X.-J. Jing, "A New Kind of Non-Acoustic Speech Acquisition Method Based on Millimeter Waveradar," Progress In Electromagnetics Research, Vol. 130, 17-40, 2012.
doi:10.2528/PIER12052207
http://www.jpier.org/PIER/pier.php?paper=12052207

References:
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

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

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.

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

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

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

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

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

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

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

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

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

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

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.

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

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

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

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

20. Guo, B. and G. Wen, "Periodic time-varying noise in current-commutating cmos mìxers," Progress In Electromagnetics Research, Vol. 117, 283-298, 2011.

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

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.

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

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

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

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

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

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

31. Dl, D. and Denoising by soft thresholding, "IEEE Trans. Inform Theory,", Vol. 41, No. 3, 613-627, 1995.

32. Fu, Q. and E. Wan, "Perceptual wavelet adaptive denoising of speech," Eurospeech, 578-580, 2003.

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

34. Sheikhzadeh, H. and H. Abutalebi, "An improved wavelet-based speech enhancement system," Eurospeech, 1855-1858, 2001.

35. Mahmoudi, D., "A microphone array for speech enhancement using multiresolution wavelet transform," Proc. of Eurospeech, 339-342, Rhodes, Greece, 1997.

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

37. Sika, J. and V. Davidek, "Multi-channel noise reduction using wavelet filter bank," Eurospeech, 2595-2598, Rhodes, Greece, 1997.

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

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.

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

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

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.

44. Shannon, C. E., "A mathematical theory of communication," Bell System Technical Journal, Vol. 27, Nos. 379-423 and 623-656, 1948.

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

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

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

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

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

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


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