{"title":"An Approach to Noise Variance Estimation in Very Low Signal-to-Noise Ratio Stochastic Signals","authors":"Miljan B. Petrovi\u0107, Du\u0161an B. Petrovi\u0107, Goran S. Nikoli\u0107","volume":111,"journal":"International Journal of Computer and Information Engineering","pagesStart":469,"pagesEnd":473,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10003872","abstract":"
This paper describes a method for AWGN (Additive White Gaussian Noise) variance estimation in noisy stochastic signals, referred to as Multiplicative-Noising Variance Estimation (MNVE). The aim was to develop an estimation algorithm with minimal number of assumptions on the original signal structure. The provided MATLAB simulation and results analysis of the method applied on speech signals showed more accuracy than standardized AR (autoregressive) modeling noise estimation technique. In addition, great performance was observed on very low signal-to-noise ratios, which in general represents the worst case scenario for signal denoising methods. High execution time appears to be the only disadvantage of MNVE. After close examination of all the observed features of the proposed algorithm, it was concluded it is worth of exploring and that with some further adjustments and improvements can be enviably powerful.<\/p>\r\n","references":"[1]\tS. Beheshti, M. A. Dahleh, \u201cOn Denoising and Signal Representation,\u201d in Proc. 10th Mediterranean Conf. Control and Automation, Lisbon, 2002.\r\n[2]\tS. Beheshti, M. A. Dahleh, \u201cNoise Variance in Signal Denoising,\u201d in Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing, vol. 6, pp. 185-188, April 2003.\r\n[3]\tM. H. Hayes, Statistical Digital Signal Processing and Modeling. John Wiley & Sons, Inc, NY: Georgia Institute of Technology, 1996.\r\n[4]\tE. Parzen, Modern Probability Theory and Its Applications. John Wiley & Sons, Inc, NY, 1960.\r\n[5]\tJ. Theiler, S. Eubank, A. Longtin, B. Galdrikian, J. D. Farmer, \u201cTesting for Nonlinearity in Time Series: The Method of Surrogate Data,\u201d Physica, vol. 58, pp. 77\u201394, March 1992.\r\n[6]\tY. Hu, P. C. Loizou, \u201cSubjective Comparison and Evaluation of Speech Enhancement Algorithms,\u201d Speech Commun, vol. 49, pp. 588\u2013601, July 2007.\r\n[7]\tK. K. Paliwal, \u201cEstimation of Noise Variance from the Noisy AR signal and Its Application in Speech Enhancement,\u201d IEEE Trans. Acoustics, Speech, and Signal Processing, vol. 36, pp. 292\u2013294, February 1988.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 111, 2016"}