• DocumentCode
    88365
  • Title

    A Family of Maximum SNR Filters for Noise Reduction

  • Author

    Gongping Huang ; Benesty, Jacob ; Tao Long ; Jingdong Chen

  • Author_Institution
    Center of Immersive & Intell. Acoust., Northwestern Polytech. Univ., Xian, China
  • Volume
    22
  • Issue
    12
  • fYear
    2014
  • fDate
    Dec. 2014
  • Firstpage
    2034
  • Lastpage
    2047
  • Abstract
    This paper is devoted to the study and analysis of the maximum signal-to-noise ratio (SNR) filters for noise reduction both in the time and short-time Fourier transform (STFT) domains with one single microphone and multiple microphones. In the time domain, we show that the maximum SNR filters can significantly increase the SNR but at the expense of tremendous speech distortion. As a consequence, the speech quality improvement, measured by the perceptual evaluation of speech quality (PESQ) algorithm, is marginal if any, regardless of the number of microphones used. In the STFT domain, the maximum SNR filters are formulated by considering the interframe information in every frequency band. It is found that these filters not only improve the SNR, but also improve the speech quality significantly. As the number of input channels increases so is the gain in SNR as well as the speech quality. This demonstrates that the maximum SNR filters, particularly the multichannel ones, in the STFT domain may be of great practical value.
  • Keywords
    Fourier transforms; filtering theory; microphones; signal denoising; PESQ algorithm; STFT domain; interframe information; maximum SNR filter; microphone; noise reduction; perceptual evaluation of speech quality algorithm; short-time Fourier transform domain; signal-to-noise ratio filter; speech distortion; speech quality improvement; Correlation; Microphones; Noise reduction; Signal to noise ratio; Speech; Vectors; Maximum SNR filter; multichannel; noise reduction; short-time Fourier transform (STFT) domain; single channel; speech enhancement; time domain;
  • fLanguage
    English
  • Journal_Title
    Audio, Speech, and Language Processing, IEEE/ACM Transactions on
  • Publisher
    ieee
  • ISSN
    2329-9290
  • Type

    jour

  • DOI
    10.1109/TASLP.2014.2360643
  • Filename
    6911982