Noise Estimation based on Standard Deviation and Sigmoid Function Using a Posteriori Signal to Noise Ratio in Nonstationary Noisy Environments

  • Published : 2008.12.31


In this paper, we propose a new noise estimation and reduction algorithm for stationary and nonstationary noisy environments. This approach uses an algorithm that classifies the speech and noise signal contributions in time-frequency bins. It relies on the ratio of the normalized standard deviation of the noisy power spectrum in time-frequency bins to its average. If the ratio is greater than an adaptive estimator, speech is considered to be present. The propose method uses an auto control parameter for an adaptive estimator to work well in highly nonstationary noisy environments. The auto control parameter is controlled by a linear function using a posteriori signal to noise ratio(SNR) according to the increase or the decrease of the noise level. The estimated clean speech power spectrum is obtained by a modified gain function and the updated noisy power spectrum of the time-frequency bin. This new algorithm has the advantages of much more simplicity and light computational load for estimating the stationary and nonstationary noise environments. The proposed algorithm is superior to conventional methods. To evaluate the algorithm's performance, we test it using the NOIZEUS database, and use the segment signal-to-noise ratio(SNR) and ITU-T P.835 as evaluation criteria.


  1. M. Bhatnagar, A Modified Spectral Subtraction Method Combined with Perceptual Weighting for Speech Enhancement, Master's Thesis, University of Texas at Dallas, 2003
  2. S. F. Boll, "Suppression of acoustic noise in speech using spectral subtraction," IEEE Trans. on Acoustics, Speech, and Signal Processing, vol. 27, no. 2, pp. 113-120, 1979
  3. Y. Ephraim and D. Malah, "Speech enhancement using a minimum mean-square error short-time spectral amplitude estimator," IEEE Trans. on Acoustics, Speech, and Signal Processing, vol. 32, no. 6, pp. 1109-1121, 1984
  4. Y. Hu, Subspace and Multitaper Methods for Speech Enhancement, Ph.D. Dissertation. University of Texas at Dallas, 2003
  5. O. Cappe, "Elimination of the musical noise phenomenon with the Ephraim and Malah noise suppressor," IEEE Trans. on Speech Audio Processing, vol. 2, no. 2, pp. 346-349, 1994
  6. R. Martin, "Noise power spectral density estimation based on optimal smoothing and minimum statistics," IEEE Trans. on Speech Audio Processing, vol. 9, no. 5, pp. 504-512, 2001
  7. R. Sundarrajan and C. L. Philipos, "A noiseestimation algorithm for highly non-stationary environments," Speech Communication, vol. 48, pp. 220-231, 2006
  8. I. Cohen, "Noise spectrum in adverse environments: Improved minima controlled recursive averaging," IEEE Trans. on Speech Audio Processing, vol. 11, no. 5, pp 466-475, 2003
  9. I. Cohen, "Speech enhancement using a noncausal a priori SNR estimator," IEEE Signal Processing Letters, vol. 11, no. 9, pp. 725-728, 2004
  10. C. L. Philipos, Speech Enhancement (Theory and Practice), 1st edition, CRC Press, Boca Raton, FL, 2007
  11. ITU-T, "Subjective test methodology for evaluating speech communication systems that include noise suppression algorithm," ITU-T Recommendation, p. 835, 2003
  12. S. J. Lee and S. H. Kim, "Speech enhancement using gain function of noisy power estimates and linear regression," Proc. of IEEE/FBIT Int. Conf. Frontiers in the Convergence of Bioscience and Information Technologies, pp. 613-616, October 2007
  13. S. Kamath and P. Loizou, "A multi-band spectral subtraction method for enhancing speech corrupted by colored noise," Proc. of International Conference on Acoustics, Speech and Signal Processing, pp. 4164-4167, 2002