• Computer Science
  • Published 2003

ESTIMATING AR PARAMETER-SETS FOR LINEAR-RECURRENT SIGNALS IN CONVOLUTIVE MIXTURES

@inproceedings{Miyoshi2003ESTIMATINGAP,
  title={ESTIMATING AR PARAMETER-SETS FOR LINEAR-RECURRENT SIGNALS IN CONVOLUTIVE MIXTURES},
  author={Masato Miyoshi},
  year={2003}
}
This article investigates a theoretical basis for estimating autoregressive (AR) processes for linear-recurrent signals in convolutive mixtures. Whitening of such signals is sometimes a problem in multichannel blind equalization which is intended to extract the original signals even if the signals are of a convolutive mixture type. This whitening is due to inverse-filtering which deconvolves the AR processes that generate the linear-recurrent signals. To avoid this excessive deconvolution, it… CONTINUE READING

Figures and Topics from this paper.

Citations

Publications citing this paper.
SHOWING 1-10 OF 15 CITATIONS

Speech Dereverberation in Short Time Fourier Transform Domain with Crossband Effect Compensation

VIEW 9 EXCERPTS
CITES BACKGROUND & METHODS

Mathematical analysis of speech dereverberation based on time-varying Gaussian source model: Its solution and convergence characteristics

VIEW 1 EXCERPT
CITES BACKGROUND

Speech Dereverberation Based on Variance-Normalized Delayed Linear Prediction

VIEW 1 EXCERPT
CITES METHODS

Blind speech dereverberation with multi-channel linear prediction based on short time fourier transform representation

VIEW 1 EXCERPT
CITES BACKGROUND

Speech Dereverberation Based on Maximum-Likelihood Estimation With Time-Varying Gaussian Source Model

VIEW 1 EXCERPT
CITES METHODS

Dereverberation and Denoising Using Multichannel Linear Prediction

VIEW 2 EXCERPTS
CITES BACKGROUND & METHODS

Importance of Energy and Spectral Features in Gaussian Source Model for Speech Dereverberation

VIEW 1 EXCERPT
CITES METHODS

Precise Dereverberation Using Multichannel Linear Prediction

VIEW 1 EXCERPT
CITES METHODS

Robust blind dereverberation of speech signals based on characteristics of short-time speech segments

VIEW 1 EXCERPT
CITES BACKGROUND

References

Publications referenced by this paper.
SHOWING 1-10 OF 21 REFERENCES

Independent Component Analysis, Tokyo: Science-Sha

  • S. Amari, N. Murata
  • 2002
VIEW 1 EXCERPT

Makino,”Time domain blind source separation of non-stationay convolved signals by utilizing geometric beamforming,

  • R. Aichner, S. Araki
  • Proc. NNSP’02,
  • 2002
VIEW 1 EXCERPT

Speech enhancement using excitation source information

VIEW 3 EXCERPTS

Linear Estimation, NJ

  • T. Kailath, A. H. Sayed, B. Hassibi
  • 2000
VIEW 1 EXCERPT