Efficient Noise Estimation for Speech Enhancement in Wavelet Packet Transform

  • Jung, Sung-Il (School of Electrical and Computer Engineering, Hanyang University) ;
  • Yang, Sung-Il (School of Electrical and Computer Engineering, Hanyang University)
  • Published : 2006.12.30

Abstract

In this paper, we suggest a noise estimation method for speech enhancement in nonstationary noisy environments. The proposed method consists of the following two main processes. First, in order to receive fewer affect of variable signals, a best fitting regression line is used, which is obtained by applying a least squares method to coefficient magnitudes in a node with a uniform wavelet packet transform. Next, in order to update the noise estimation efficiently, a differential forgetting factor and a correlation coefficient per subband are used, where subband is employed for applying the weighted value according to the change of signals. In particular, this method has the ability to update the noise estimation by using the estimated noise at the previous frame only, without utilizing the statistical information of long past frames and explicit nonspeech frames by voice activity detector. In objective assessments, it was observed that the performance of the proposed method was better than that of the compared (minima controlled recursive averaging, weighted average) methods. Furthermore, the method showed a reliable result even at low SNR.

Keywords

References

  1. B. Carnero and A. Drygajlo, 'Perceptual speech coding and enhancement using frame-synchronized fast wavelet packet transform algorithm,' IEEE Trans. Signal Processing, 47(6) 1622-1635, Jun. 1999 https://doi.org/10.1109/78.765133
  2. S. F. Boll, 'Suppression of acoustic noise in speech using spectral subtraction,' IEEE Trans. Acoustic Speech Signal Processing, vol, ASSP-27, 2 113-120, Apr. 1979
  3. N. Virag, 'Single channel speech enhancement based on masking properties of the human auditory system,' IEEE Trans. Speech and Audio Processing, 7(2) 126-137, Mar. 1999 https://doi.org/10.1109/89.748118
  4. R. Martin, 'Spectral subtraction based on minimum statistics, 'EUROSPEECH, 1182-1185, Sept. 1994
  5. G. Doblinger, 'Computationally efficient speech enhancement by spectral minima tracking in subbands,' EUROSPEECH, 1513-1516, Sept. 1995
  6. I. Cohen and B. Berdugo, 'Noise estimation by minima controlled recursive averaging for robust speech enhancement,' IEEE Signal Process. Lett., 9(1) 12-15, Jan. 2002 https://doi.org/10.1109/97.988717
  7. I. Cohen, 'Noise spectrum estimation in adverse environments: improved minima controlled recursive averaging,' IEEE Trans. Speech and Audio Processing, 11(5) 466-475, Sept. 2003 https://doi.org/10.1109/TSA.2003.811544
  8. S. Rangachari, P. C. Loizou, and Y. Hu, 'A noise estimation algorithm with rapid adaptation for highly non-stationary environments,' IEEE ICASSP, 305-308, May 2004
  9. Z. Lin and R. Goubran, 'Instant noise estimation using Fourier transform of AMDF and variable start minima search,' IEEE ICASSP, 161-164, Mar. 2005
  10. H. G. Hirsh and C. Ehrlicher, 'Noise estimation techniques for robust speech recognition,' IEEE ICASSP, 153-156, May 1995
  11. T. K. Moon and W. C. Stirling, Mathematical methods and algorithms for signal processing, Upper Saddle River, (NJ: PrenticeHall, 2000)
  12. J. R. Deller, J. G. Proakis, and J. H. L. Hansen, Discrete-time processing of speech signals, Englewood Cliffs, (NJ: Prentice-Hall, 1993)
  13. S. Mallat, A wavelet tour of signal processing, (2nd Ed., Academic Press, 1999)
  14. N. Virag, 'Single channel speech enhancement based on masking properties of the human auditory system,' IEEE Trans. Speech Audio Processing, 7(2) 126-137, Mar. 1999 https://doi.org/10.1109/89.748118