Subband particle filtering for speech enhancement

  title={Subband particle filtering for speech enhancement},
  author={Ying Deng and V. John Mathews},
  journal={2006 14th European Signal Processing Conference},
Particle filters have recently been applied to speech enhancement when the input speech signal is modeled as a time-varying autoregressive process with stochastically evolving parameters. This type of modeling results in a nonlinear and conditionally Gaussian state-space system that is not amenable to analytical solutions. Prior work in this area involved signal processing in the fullband domain and assumed white Gaussian noise with known variance. This paper extends such ideas to subband… CONTINUE READING


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Publications referenced by this paper.
Showing 1-10 of 30 references

Bayesian Theory

J. M. Bernardo, A.F.M. Smith
New York: John Wiley & Sons • 1994
View 6 Excerpts
Highly Influenced

Beyond the Kalman Filter: Particle Filters for Tracking Applications

B. Ristic, S. Arulampalam, N. Gordon
Norwood, MA: Artech House • 2004
View 1 Excerpt


A. Doucet
de Freitas and N.J. Gordon, Sequential Monte Carlo Methods in Practice . New York: Springer • 2001
View 3 Excerpts

Durrant-White, “A new method for nonlinear transformation of means and covariance in filters and estimators,”IEEE

S. Julier, H.F.J. Uhlmann
Trans. Automatic Control , • 2000
View 1 Excerpt

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