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QU Zhi, ZHANG Bohu. An improved wavelet threshold algorithm applied in laser interception[J]. LASER TECHNOLOGY, 2014, 38(2): 218-224. DOI: 10.7510/jgjs.issn.1001-3806.2014.02.016
Citation: QU Zhi, ZHANG Bohu. An improved wavelet threshold algorithm applied in laser interception[J]. LASER TECHNOLOGY, 2014, 38(2): 218-224. DOI: 10.7510/jgjs.issn.1001-3806.2014.02.016

An improved wavelet threshold algorithm applied in laser interception

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  • Received Date: June 03, 2013
  • Revised Date: July 05, 2013
  • Published Date: February 24, 2014
  • In order to get better denoising result in laser interception, an improved wavelet threshold denoising algorithm was proposed. Through theoretical analysis and experimental verification, a series of simulation data were obtained. The results show that, compared with the traditional denoising algorithm, the speech signal-to-noise ratio after denoising is improved greatly. Denoising effect is obvious, signal waveform is smoother and distortion is less.
  • [1]
    GUO Ch Y, ZHENG K. Denoising optical interferometry signal based on wavelet transform threshold [J]. Laser Technology, 2009, 37 (5): 506-508(in Chinese).
    [2]
    WANG B, LI J W, WANG Zh F. Threshold de-noising method based on wavelet analysis [J]. Computer Engineering and Design, 2011, 32 (3): 1099-1102(in Chinese).
    [3]
    GUO Ch X.On the speaker recognition algorithm [J]. Journal of Xi'an University of Post and Telecom, 2010, 15 (5): 104-106(in Chinese).
    [4]
    ZHENG R, ZHANG Sh W, XU B. Improvement of speaker identification by combining prosodic features with acoustic features [C]// 5th Chinese Conference on Biometric Recognition.Guangzhou: Lecture Notes in Computer Science, 2004: 569-576.
    [5]
    YE H Sh, TAO J X, ZHANG D W. Improve speaker identification performance by integrating characters under noisy conditions [J].Computer Simulation, 2009, 26(3): 325-328(in Chinese).
    [6]
    GAN Zh G. An improved feature extraction method in speaker identification[C]//2011 Third International Conference on Intelligent Human-Machine Systems and Cybernetics.Hangzhou:IEEE, 2011: 218-222.
    [7]
    McLAREN M, van LEEUWEN D. Source normalised and weighted LDA for robust speaker recognition using I-vectors[C]//IEEE International Conference on Acoustics, Speech and Signal Processing. Prague,Czech Republic:IEEE,2011: 5456-5459.
    [8]
    DU J, ZOU X, HAO J, et al. The efficiency of ICA-based representation analysis:application to speech feature extraction[J].Chinese Journal of Electronics, 2011, 20(2): 287-292.
    [9]
    ZHENG J W, WANG W L, ZHENG Z P. Speaker identification approach of hybrid GMM and RVM [J]. Computer Simulation, 2010, 36(15): 168-170(in Chinese).
    [10]
    SAASTAMOINEN J, KARPOV E, HAUTAMAKI V, et al. Accuracy of MFCC-based speaker recognition in series 60 device [J]. EURASIP Journal on Advances in Signal Processing, 2005, 37(17): 2816-2827.
    [11]
    WOOTERS C, HUIJBREGTS M. The ICSI RT07s speaker diarization system [J]. Multimodal Technologies for Perception of Humans, 2008, 46(25): 509-519.
    [12]
    BOLL S. Suppression of acoustic noise in speech using spectral subtraction [J]. IEEE Transactions on Acoustics, Speech and Signal Processing, 1979, 27(2): 113-120.
    [13]
    CHEN A M, VASEGHI S, MECOURT P. State based sub-band LP wiener filters for speech enhancement in car environments [J]. IEEE Proceedings of International Conference on Acoustics, Speech and Signal Processing,2000,15(1):213-216.
    [14]
    McAULAY R, MALPASS M. Speech enhancement using a soft-decision noise suppression filter [J]. IEEE Transaction on Acoustics, Speech and Signal Processing,1980,28(2):137-145.

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