The University of Southampton
University of Southampton Institutional Repository

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, , (doi:10.1007/978-1-84996-056-4_8)) 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.

Full text not available from this repository.

More information

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

Catalogue record

Date deposited: 04 May 2020 16:31
Last modified: 23 May 2020 00:47

Export record

Altmetrics

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×