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Speech dereverberation performance of a polynomial-EVD subspace approach

Speech dereverberation performance of a polynomial-EVD subspace approach
Speech dereverberation performance of a polynomial-EVD subspace approach

The degradation of speech arising from additive background noise and reverberation affects the performance of important speech applications such as telecommunications, hearing aids, voice-controlled systems and robot audition. In this work, we focus on dereverberation. It is shown that the parameterized polynomial matrix eigenvalue decomposition (PEVD)-based speech enhancement algorithm exploits the lack of correlation between speech and the late reflections to enhance the speech component associated with the direct path and early reflections. The algorithm's performance is evaluated using simulations involving measured acoustic impulse responses and noise from the ACE corpus. The simulations and informal listening examples have indicated that the PEVD-based algorithm performs dereverberation over a range of SNRs without introducing any noticeable processing artefacts.

Broadband signal processing, Convolutive noise, Dereverberation, Microphone array, Polynomial matrix eigenvalue decomposition
2219-5491
221-225
European Signal Processing Conference, EUSIPCO
Neo, Vincent W.
7ec5cc5f-8248-40ec-8864-b31335d4ddf2
Evers, Christine
93090c84-e984-4cc3-9363-fbf3f3639c4b
Naylor, Patrick A.
13079486-664a-414c-a1a2-01a30bf0997b
Neo, Vincent W.
7ec5cc5f-8248-40ec-8864-b31335d4ddf2
Evers, Christine
93090c84-e984-4cc3-9363-fbf3f3639c4b
Naylor, Patrick A.
13079486-664a-414c-a1a2-01a30bf0997b

Neo, Vincent W., Evers, Christine and Naylor, Patrick A. (2021) Speech dereverberation performance of a polynomial-EVD subspace approach. In 28th European Signal Processing Conference, EUSIPCO 2020 - Proceedings. vol. 2021-January, European Signal Processing Conference, EUSIPCO. pp. 221-225 . (doi:10.23919/Eusipco47968.2020.9287869).

Record type: Conference or Workshop Item (Paper)

Abstract

The degradation of speech arising from additive background noise and reverberation affects the performance of important speech applications such as telecommunications, hearing aids, voice-controlled systems and robot audition. In this work, we focus on dereverberation. It is shown that the parameterized polynomial matrix eigenvalue decomposition (PEVD)-based speech enhancement algorithm exploits the lack of correlation between speech and the late reflections to enhance the speech component associated with the direct path and early reflections. The algorithm's performance is evaluated using simulations involving measured acoustic impulse responses and noise from the ACE corpus. The simulations and informal listening examples have indicated that the PEVD-based algorithm performs dereverberation over a range of SNRs without introducing any noticeable processing artefacts.

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More information

Published date: 24 January 2021
Additional Information: Funding Information: † The research leading to these results is funded through the UK EPSRC Fellowship grant no. EP/P001017/1 while the author was with the Department of Electrical and Electronic Engineering, Imperial College London. ∗ This work is funded through the UK EPSRC grant no. EP/S035842/1. Publisher Copyright: © 2021 European Signal Processing Conference, EUSIPCO. All rights reserved. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
Venue - Dates: 28th European Signal Processing Conference, EUSIPCO 2020, , Amsterdam, Netherlands, 2020-08-23 - 2020-08-27
Keywords: Broadband signal processing, Convolutive noise, Dereverberation, Microphone array, Polynomial matrix eigenvalue decomposition

Identifiers

Local EPrints ID: 450390
URI: http://eprints.soton.ac.uk/id/eprint/450390
ISSN: 2219-5491
PURE UUID: 55a35c71-65e4-448d-b2da-4360b97d33e1
ORCID for Christine Evers: ORCID iD orcid.org/0000-0003-0757-5504

Catalogue record

Date deposited: 27 Jul 2021 16:30
Last modified: 26 Nov 2021 03:21

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Contributors

Author: Vincent W. Neo
Author: Christine Evers ORCID iD
Author: Patrick A. Naylor

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