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Parametric modelling for single-channel blind dereverberation of speech from a moving speaker

Parametric modelling for single-channel blind dereverberation of speech from a moving speaker
Parametric modelling for single-channel blind dereverberation of speech from a moving speaker
Single-channel blind dereverberation for the enhancement of speech acquired in acoustic environments is essential in applications where microphone arrays prove impractical. In many scenarios, the source-sensor geometry is not varying rapidly, but in most applications the geometry is subject to change, for example when a user wishes to move around a room. A previous model-based approach to blind dereverberation by representing the channel as a linear time-varying all-pole filter is extended, in which the parameters of the filter are modelled as a linear combination of known basis functions with unknown weightings. Moreover, an improved block-based time-varying autoregressive model is proposed for the speech signal, which aims to reflect the underlying signal statistics more accurately on both a local and global level. Given these parametric models, their coefficients are estimated using Bayesian inference, so that the channel estimate can then be used for dereverberation. An in-depth discussion is also presented about the applicability of these models to real speech and a real acoustic environment. Results are presented to demonstrate the performance of the Bayesian inference algorithms.
1751-9675
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. (2008) Parametric modelling for single-channel blind dereverberation of speech from a moving speaker. IET Signal Processing, 2 (2). (doi:10.1049/iet-spr:20070046).

Record type: Article

Abstract

Single-channel blind dereverberation for the enhancement of speech acquired in acoustic environments is essential in applications where microphone arrays prove impractical. In many scenarios, the source-sensor geometry is not varying rapidly, but in most applications the geometry is subject to change, for example when a user wishes to move around a room. A previous model-based approach to blind dereverberation by representing the channel as a linear time-varying all-pole filter is extended, in which the parameters of the filter are modelled as a linear combination of known basis functions with unknown weightings. Moreover, an improved block-based time-varying autoregressive model is proposed for the speech signal, which aims to reflect the underlying signal statistics more accurately on both a local and global level. Given these parametric models, their coefficients are estimated using Bayesian inference, so that the channel estimate can then be used for dereverberation. An in-depth discussion is also presented about the applicability of these models to real speech and a real acoustic environment. Results are presented to demonstrate the performance of the Bayesian inference algorithms.

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Published date: June 2008

Identifiers

Local EPrints ID: 439801
URI: http://eprints.soton.ac.uk/id/eprint/439801
ISSN: 1751-9675
PURE UUID: bc082d86-e13e-409d-aaf7-29d12ac1f80c
ORCID for Christine Evers: ORCID iD orcid.org/0000-0003-0757-5504

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Date deposited: 05 May 2020 16:30
Last modified: 23 May 2020 00:47

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Contributors

Author: Christine Evers ORCID iD
Author: James R. Hopgood

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