Detail publikace

Estimation of Power Spectral Density using Wavelet Thresholding

SYSEL, P. MIŠUREC, J.

Originální název

Estimation of Power Spectral Density using Wavelet Thresholding

Anglický název

Estimation of Power Spectral Density using Wavelet Thresholding

Jazyk

en

Originální abstrakt

The basic problem of the single-channel speech enhancement methods lies in a rapid and precise method for estimating noise, on which the quality of enhancement method depends. The paper describes a new method of power spectral density estimation using wavelet transform in spectral domain. To reduce periodogram variance the proposed method use the procedure of thresholding the wavelet coefficients of a periodogram. Then the smoothed estimate of power spectral density of noise is obtained using the inverse discrete wavelet transform.

Anglický abstrakt

The basic problem of the single-channel speech enhancement methods lies in a rapid and precise method for estimating noise, on which the quality of enhancement method depends. The paper describes a new method of power spectral density estimation using wavelet transform in spectral domain. To reduce periodogram variance the proposed method use the procedure of thresholding the wavelet coefficients of a periodogram. Then the smoothed estimate of power spectral density of noise is obtained using the inverse discrete wavelet transform.

Dokumenty

BibTex


@article{BUT49594,
  author="Petr {Sysel} and Jiří {Mišurec}",
  title="Estimation of Power Spectral Density using Wavelet Thresholding",
  annote="The basic problem of the single-channel speech enhancement methods lies in a rapid and precise method for estimating noise, on which the quality of enhancement method depends. The paper describes a new method of power spectral density estimation using wavelet transform in spectral domain. To reduce periodogram variance the proposed method use the procedure of thresholding the wavelet coefficients of a periodogram. Then the smoothed estimate of power spectral density of noise is obtained using the inverse discrete wavelet transform.",
  address="WSEAS Press",
  chapter="49594",
  institution="WSEAS Press",
  journal="WSEAS Applied Informatics & Communications",
  number="1",
  volume="2008",
  year="2008",
  month="december",
  pages="207--211",
  publisher="WSEAS Press",
  type="journal article in Web of Science"
}