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Marginalization of static observation parameters in a rao-blackwellized particle filter with application to sequential blind speech dereverberation

Marginalization of static observation parameters in a rao-blackwellized particle filter with application to sequential blind speech dereverberation
Marginalization of static observation parameters in a rao-blackwellized particle filter with application to sequential blind speech dereverberation

Enhancement of an unknown signal from distorted observations is an extremely important Engineering problem. In addition to noise, the observation space often contains a degrading filter component. A typical example is blind speech enhancement, where a reverberant channel between a stationary source and the receiver can be modeled as a static infinite impulse response component. Particle filters have become popular and versatile estimators for estimating the clean source signal and unknown model parameters by sequentially drawing a large number of samples from a hypothesis distribution. However, direct sampling of static components leads to particle impoverishment as a dynamic is implicitly enforced on the parameters. To circumvent this issue, this paper proposes a novel approach by exploiting analytically tractable substructures of the state space to marginalize static components, facilitating separate estimation of the static parameters using their optimal estimator. The approach is tested for blind dereverberation of speech. Results show that the proposed algorithm effectively removes the effects of the static reverberant channel.

2219-5491
1437-1441
Evers, Christine
93090c84-e984-4cc3-9363-fbf3f3639c4b
Hopgood, James R.
ae180a4d-33bf-468d-ab66-5eeb152e7fc2
Evers, Christine
93090c84-e984-4cc3-9363-fbf3f3639c4b
Hopgood, James R.
ae180a4d-33bf-468d-ab66-5eeb152e7fc2

Evers, Christine and Hopgood, James R. (2009) Marginalization of static observation parameters in a rao-blackwellized particle filter with application to sequential blind speech dereverberation. In European Signal Processing Conference. pp. 1437-1441 .

Record type: Conference or Workshop Item (Paper)

Abstract

Enhancement of an unknown signal from distorted observations is an extremely important Engineering problem. In addition to noise, the observation space often contains a degrading filter component. A typical example is blind speech enhancement, where a reverberant channel between a stationary source and the receiver can be modeled as a static infinite impulse response component. Particle filters have become popular and versatile estimators for estimating the clean source signal and unknown model parameters by sequentially drawing a large number of samples from a hypothesis distribution. However, direct sampling of static components leads to particle impoverishment as a dynamic is implicitly enforced on the parameters. To circumvent this issue, this paper proposes a novel approach by exploiting analytically tractable substructures of the state space to marginalize static components, facilitating separate estimation of the static parameters using their optimal estimator. The approach is tested for blind dereverberation of speech. Results show that the proposed algorithm effectively removes the effects of the static reverberant channel.

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More information

Published date: 1 December 2009
Venue - Dates: 17th European Signal Processing Conference, EUSIPCO 2009, , Glasgow, United Kingdom, 2009-08-23 - 2009-08-27

Identifiers

Local EPrints ID: 445882
URI: http://eprints.soton.ac.uk/id/eprint/445882
ISSN: 2219-5491
PURE UUID: 9e9ad7cd-a20e-4bd0-aeaa-0ad26b901cb0
ORCID for Christine Evers: ORCID iD orcid.org/0000-0003-0757-5504

Catalogue record

Date deposited: 13 Jan 2021 17:30
Last modified: 18 Feb 2021 17:41

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