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Speech enhancement using polynomial eigenvalue decomposition

Speech enhancement using polynomial eigenvalue decomposition
Speech enhancement using polynomial eigenvalue decomposition
Speech enhancement is important for applications such as telecommunications, hearing aids, automatic speech recognition and voice-controlled system. The enhancement algorithms aim to reduce interfering noise while minimizing any speech distortion. In this work for speech enhancement, we propose to use polynomial matrices in order to exploit the spatial, spectral as well as temporal correlations between the speech signals received by the microphone array. Polynomial matrices provide the necessary mathematical framework in order to exploit constructively the spatial correlations within and between sensor pairs, as well as the spectral-temporal correlations of broadband signals, such as speech. Specifically, the polynomial eigenvalue decomposition (PEVD) decorrelates simultaneously in space, time and frequency. We then propose a PEVD-based speech enhancement algorithm. Simulations and informal listening examples have shown that our method achieves noise reduction without introducing artefacts into the enhanced signal for white, babble and factory noise conditions between -10 dB to 30 dB SNR.
IEEE
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. (2019) Speech enhancement using polynomial eigenvalue decomposition. In Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA). IEEE.. (doi:10.1109/WASPAA.2019.8937235).

Record type: Conference or Workshop Item (Paper)

Abstract

Speech enhancement is important for applications such as telecommunications, hearing aids, automatic speech recognition and voice-controlled system. The enhancement algorithms aim to reduce interfering noise while minimizing any speech distortion. In this work for speech enhancement, we propose to use polynomial matrices in order to exploit the spatial, spectral as well as temporal correlations between the speech signals received by the microphone array. Polynomial matrices provide the necessary mathematical framework in order to exploit constructively the spatial correlations within and between sensor pairs, as well as the spectral-temporal correlations of broadband signals, such as speech. Specifically, the polynomial eigenvalue decomposition (PEVD) decorrelates simultaneously in space, time and frequency. We then propose a PEVD-based speech enhancement algorithm. Simulations and informal listening examples have shown that our method achieves noise reduction without introducing artefacts into the enhanced signal for white, babble and factory noise conditions between -10 dB to 30 dB SNR.

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Published date: 2019

Identifiers

Local EPrints ID: 439802
URI: http://eprints.soton.ac.uk/id/eprint/439802
PURE UUID: c4e110e4-d811-43d6-9d41-7cd1808160f7
ORCID for Christine Evers: ORCID iD orcid.org/0000-0003-0757-5504

Catalogue record

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

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Contributors

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

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