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Bayesian single channel blind dereverberation of speech from a moving talker

Bayesian single channel blind dereverberation of speech from a moving talker
Bayesian single channel blind dereverberation of speech from a moving talker
This chapter discusses a model-based framework for single-channel blind dereverberation of speech, in which parametric models are used to represent both the unknown source and the unknown acoustic channel. The parameters of the entire model are estimated using the Bayesian paradigm, and an estimate of the source signal is found by either inverse filtering of the observed signal with the estimated channel coefficients, or directly within a sequential framework. Model-based approaches fundamentally rely on the availability of realistic and tractable models that reflect the underlying speech process and acoustic systems. The choice of these models is extremely important and is discussed in detail, with a focus on spatially varying room impulse responses. The mathematical framework and methodology for parameter estimation and dereverberation is also discussed. Some examples of the proposed approaches are presented with results.
219-270
Springer
Hopgood, James R.
ae180a4d-33bf-468d-ab66-5eeb152e7fc2
Evers, Christine
93090c84-e984-4cc3-9363-fbf3f3639c4b
Fortune, Steven
ffdb2288-4d4d-40a0-b8cb-db7135f1be25
Hopgood, James R.
ae180a4d-33bf-468d-ab66-5eeb152e7fc2
Evers, Christine
93090c84-e984-4cc3-9363-fbf3f3639c4b
Fortune, Steven
ffdb2288-4d4d-40a0-b8cb-db7135f1be25

Hopgood, James R., Evers, Christine and Fortune, Steven (2010) Bayesian single channel blind dereverberation of speech from a moving talker. In, Speech Dereverberation. (Signals and Commmunication Technology) Springer, pp. 219-270. (doi:10.1007/978-1-84996-056-4_8).

Record type: Book Section

Abstract

This chapter discusses a model-based framework for single-channel blind dereverberation of speech, in which parametric models are used to represent both the unknown source and the unknown acoustic channel. The parameters of the entire model are estimated using the Bayesian paradigm, and an estimate of the source signal is found by either inverse filtering of the observed signal with the estimated channel coefficients, or directly within a sequential framework. Model-based approaches fundamentally rely on the availability of realistic and tractable models that reflect the underlying speech process and acoustic systems. The choice of these models is extremely important and is discussed in detail, with a focus on spatially varying room impulse responses. The mathematical framework and methodology for parameter estimation and dereverberation is also discussed. Some examples of the proposed approaches are presented with results.

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Published date: 2010

Identifiers

Local EPrints ID: 439789
URI: http://eprints.soton.ac.uk/id/eprint/439789
PURE UUID: 2b280121-242c-4e19-9431-ec2c6b98aa0f
ORCID for Christine Evers: ORCID iD orcid.org/0000-0003-0757-5504

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Date deposited: 04 May 2020 16:31
Last modified: 17 Mar 2024 04:01

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Contributors

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

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