"0"^^ . . "1"^^ . . "0"^^ . . "Wiener Filtering with Spectrum Estimation by Wavelet Transformation"@en . . . "Current methods used to improve the signal\u2013to\u2013noise ratio mainly employ the Wiener filtering, or methods derived from it, such as spectral subtraction. All these methods assume that it is possible to determine or at least to estimate noise spectral characteristics. As can be derived, the estimation by the periodogram is not exact, but it contains a disturbance. Averaging the power spectra offers better results but the properties are simultaneously downgraded by a non-stationary disturbance. That is why the paper deals with the improvement of the power spectral density (PSD) estimation. To achieve this purpose we use the method of thresholding wavelet\u2013transform coefficients, which we apply to each periodogram separately. Then the resultant estimation is used for the Wiener filter. The differences between the estimations obtained by the periodogram, by averaging and by this new method are shown in the paper. When we use wavelet transformation, a marked improvement in suppressing the disturbing signal a"@en . "1"^^ . "Slovensk\u00E1 technick\u00E1 univerzita v Bratislave. Fakulta elektrotechniky a informatiky" . . . "Wiener Filtering with Spectrum Estimation by Wavelet Transformation" . "Bratislava" . "0-7803-6490-2" . "Bratislava, Slovensko" . . . "RIV/00216305:26220/01:PU20798" . . "Wiener Filtering with Spectrum Estimation by Wavelet Transformation"@en . . "26220" . "4"^^ . "Wiener, filter, wavelet, estimation"@en . . . "Wiener Filtering with Spectrum Estimation by Wavelet Transformation" . "P(GA102/00/1084), Z(MSM 262200011)" . "Current methods used to improve the signal\u2013to\u2013noise ratio mainly employ the Wiener filtering, or methods derived from it, such as spectral subtraction. All these methods assume that it is possible to determine or at least to estimate noise spectral characteristics. As can be derived, the estimation by the periodogram is not exact, but it contains a disturbance. Averaging the power spectra offers better results but the properties are simultaneously downgraded by a non-stationary disturbance. That is why the paper deals with the improvement of the power spectral density (PSD) estimation. To achieve this purpose we use the method of thresholding wavelet\u2013transform coefficients, which we apply to each periodogram separately. Then the resultant estimation is used for the Wiener filter. The differences between the estimations obtained by the periodogram, by averaging and by this new method are shown in the paper. When we use wavelet transformation, a marked improvement in suppressing the disturbing signal a" . . "[8DA7425F0212]" . "Proceedings of International Conference on Trends in Communications" . "702260" . "471-474" . . "2001-07-04+02:00"^^ . . . "RIV/00216305:26220/01:PU20798!RIV/2002/GA0/262202/N" . . "Sysel, Petr" . .