• DocumentCode
    34576
  • Title

    Decay Rate Estimators and Their Performance for Blind Reverberation Time Estimation

  • Author

    Schuldt, C. ; Handel, Peter

  • Author_Institution
    Dept. of Signal Process., R. Inst. of Technol. KTH, Stockholm, Sweden
  • Volume
    22
  • Issue
    8
  • fYear
    2014
  • fDate
    Aug. 2014
  • Firstpage
    1274
  • Lastpage
    1284
  • Abstract
    Several approaches for blind estimation of reverberation time have been presented in the literature and decay rate estimation is an integral part of many, if not all, of such approaches. This paper provides both an analytical and experimental comparison, in terms of the bias and variance of three common decay rate estimators; a straight-forward linear regression approach as well as two maximum-likelihood based methods. Situations with and without interfering additive noise are considered. It is shown that the linear regression based approach is unbiased if no smoothing is applied, and that the estimation variance in the absence of noise is constantly about twice that of the maximum-likelihood based methods. It is shown that the methods that do not take possible noise into account suffer from similar estimation bias in the presence of noise. Further, a hybrid method, combining the noise robustness and low computational complexity advantages of the two different maximum-likelihood based methods, is presented.
  • Keywords
    computational complexity; maximum likelihood estimation; regression analysis; reverberation; speech processing; blind reverberation time estimation; computational complexity; decay rate estimators; estimation bias; estimation variance; linear regression-based approach; maximum-likelihood-based method; noise robustness; straight-forward linear regression approach; Maximum likelihood estimation; Noise; Noise robustness; Reverberation; Speech; Speech processing; Blind estimation; decay rate; maximum-likelihood estimation; reverberation time;
  • fLanguage
    English
  • Journal_Title
    Audio, Speech, and Language Processing, IEEE/ACM Transactions on
  • Publisher
    ieee
  • ISSN
    2329-9290
  • Type

    jour

  • DOI
    10.1109/TASLP.2014.2328174
  • Filename
    6824831