Detail publikace

Power Spectral Density Noise Estimation using Wavelet Transform in Spectral Domain

Originální název

Power Spectral Density Noise Estimation using Wavelet Transform in Spectral Domain

Anglický název

Power Spectral Density Noise Estimation using Wavelet Transform in Spectral Domain

Jazyk

en

Originální abstrakt

The paper describes the design of a new method of power spectral density estimation using the wavelet transform in the spectral domain. To reduce periodogram variance the proposed method uses the procedure of thresholding the wavelet coefficients of a periodogram. The periodogram is decomposed into a chosen number of levels using the discrete wavelet transform with the appropriate mother wavelet. Then the thresholds used for the wavelet transform coefficients in dependence on the level are calculated. To threshold the wavelet transform coefficients soft thesholding is used. Then the smoothed estimate of power spectral density of noise is obtained using the inverse discrete wavelet transform. This reduces the variance of the estimate of power spectral density of noise.

Anglický abstrakt

The paper describes the design of a new method of power spectral density estimation using the wavelet transform in the spectral domain. To reduce periodogram variance the proposed method uses the procedure of thresholding the wavelet coefficients of a periodogram. The periodogram is decomposed into a chosen number of levels using the discrete wavelet transform with the appropriate mother wavelet. Then the thresholds used for the wavelet transform coefficients in dependence on the level are calculated. To threshold the wavelet transform coefficients soft thesholding is used. Then the smoothed estimate of power spectral density of noise is obtained using the inverse discrete wavelet transform. This reduces the variance of the estimate of power spectral density of noise.

Dokumenty

BibTex


@inproceedings{BUT25587,
  author="Petr {Sysel} and Zdeněk {Smékal}",
  title="Power Spectral Density Noise Estimation using Wavelet Transform in Spectral Domain",
  annote="The paper describes the design of a new method of power spectral density estimation using the wavelet transform in the spectral domain. To reduce periodogram variance the proposed method uses the procedure of thresholding the wavelet coefficients of a periodogram. The periodogram is decomposed into a chosen number of levels using the discrete wavelet transform with the appropriate mother wavelet. Then the thresholds used for the wavelet transform coefficients in dependence on the level are calculated. To threshold the wavelet transform coefficients soft thesholding is used. Then the smoothed estimate of power spectral density of noise is obtained using the inverse discrete wavelet transform. This reduces the variance of the estimate of power spectral density of noise.",
  booktitle="Proceedings of 17th Czech-German Workshop Speech Processing",
  chapter="25587",
  year="2007",
  month="september",
  pages="98--105",
  type="conference paper"
}