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Polynomial matrix eigenvalue decomposition-based source separation using informed spherical microphone arrays

Polynomial matrix eigenvalue decomposition-based source separation using informed spherical microphone arrays
Polynomial matrix eigenvalue decomposition-based source separation using informed spherical microphone arrays
Audio source separation is essential for many applications such as hearing aids, telecommunications, and robot audition. Subspace decomposition approaches using polynomial matrix eigenvalue decomposition (PEVD) algorithms applied to the microphone signals, or lower-dimension eigenbeams for spherical microphone arrays, are effective for speech enhancement. In this work, we extend the work from speech enhancement and propose a PEVD subspace algorithm that uses eigenbeams for source separation. The proposed PEVD-based source separation approach performs comparably with state-of-the-art algorithms, such as those based on independent component analysis (ICA) and multi-channel non-negative matrix factorization (MNMF). Informal listening examples also indicate that our method does not introduce any audible artifacts.
IEEE
Neo, Vincent W.
7ec5cc5f-8248-40ec-8864-b31335d4ddf2
Evers, Christine
93090c84-e984-4cc3-9363-fbf3f3639c4b
Naylor, Patrick
8c20a1a0-4507-4a0f-8324-f3075354dc52
Neo, Vincent W.
7ec5cc5f-8248-40ec-8864-b31335d4ddf2
Evers, Christine
93090c84-e984-4cc3-9363-fbf3f3639c4b
Naylor, Patrick
8c20a1a0-4507-4a0f-8324-f3075354dc52

Neo, Vincent W., Evers, Christine and Naylor, Patrick (2021) Polynomial matrix eigenvalue decomposition-based source separation using informed spherical microphone arrays. In IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA). IEEE. 5 pp . (In Press)

Record type: Conference or Workshop Item (Paper)

Abstract

Audio source separation is essential for many applications such as hearing aids, telecommunications, and robot audition. Subspace decomposition approaches using polynomial matrix eigenvalue decomposition (PEVD) algorithms applied to the microphone signals, or lower-dimension eigenbeams for spherical microphone arrays, are effective for speech enhancement. In this work, we extend the work from speech enhancement and propose a PEVD subspace algorithm that uses eigenbeams for source separation. The proposed PEVD-based source separation approach performs comparably with state-of-the-art algorithms, such as those based on independent component analysis (ICA) and multi-channel non-negative matrix factorization (MNMF). Informal listening examples also indicate that our method does not introduce any audible artifacts.

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More information

Accepted/In Press date: 14 July 2021

Identifiers

Local EPrints ID: 450813
URI: http://eprints.soton.ac.uk/id/eprint/450813
PURE UUID: 9f5d0917-3a3b-48a9-9812-93d5d6f114ec
ORCID for Christine Evers: ORCID iD orcid.org/0000-0003-0757-5504

Catalogue record

Date deposited: 12 Aug 2021 16:31
Last modified: 13 Aug 2021 02:02

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Contributors

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

University divisions

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