• DocumentCode
    2792809
  • Title

    An effective pitch detection method for speech signals with low signal-to-noise ratio

  • Author

    Zhao, Zhen-dong ; Hu, Xi-mei ; Tian, Jing-feng

  • Author_Institution
    Dept. of Electron. & Commun. Eng., North China Electr. Power Univ., Baoding
  • Volume
    5
  • fYear
    2008
  • fDate
    12-15 July 2008
  • Firstpage
    2775
  • Lastpage
    2778
  • Abstract
    Pitch detection in noisy environment plays an important role in speech analyzing and recognition. In this paper, an effective pitch detection method is proposed. Noised speech is denoised by an improved form of spectral subtraction method. A linear predictive coding analysis is performed on the segmented speech, and the segmented speech is filtered by the inverse filter to give the linear prediction error. The cepstrum of the linear prediction error and the autocorrelation function of the cepstrum are calculated. The result of the simulation shows that compared with the autocorrelation function pitch detection method, a distinct improvement in effect can be seen by using this improved method in pitch detection.
  • Keywords
    cepstral analysis; correlation methods; linear predictive coding; signal denoising; signal detection; speech processing; autocorrelation function pitch detection method; cepstrum autocorrelation function; inverse filter; linear prediction error; linear predictive coding analysis; noised speech; noisy environment; segmented speech; signal denoising; signal-to-noise ratio; spectral subtraction method; speech analysis; speech recognition; speech signals; Autocorrelation; Cepstral analysis; Cepstrum; Linear predictive coding; Nonlinear filters; Signal to noise ratio; Speech analysis; Speech coding; Speech recognition; Working environment noise; Pitch detection; autocorrelation function; cepstrum; linear prediction error; spectral subtraction;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2008 International Conference on
  • Conference_Location
    Kunming
  • Print_ISBN
    978-1-4244-2095-7
  • Electronic_ISBN
    978-1-4244-2096-4
  • Type

    conf

  • DOI
    10.1109/ICMLC.2008.4620879
  • Filename
    4620879