Online Parametric NMF for Speech Enhancement

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Resumé

In this paper, we propose a speech enhancement method based on non-negative matrix factorization (NMF) techniques. NMF techniques allow us to approximate the power spectral density (PSD) of the noisy signal as a weighted linear combination of trained speech and noise basis vectors arranged as the columns of a matrix. In this work, we propose to use basis vectors that are parameterised by autoregressive (AR) coefficients. Parametric representation of the spectral basis is beneficial as it can encompass the signal characteristics like, e.g. the speech production model. It is observed that the parametric representation of basis vectors is beneficial while performing online speech enhancement in low delay scenarios.
OriginalsprogEngelsk
Titel2018 26th European Signal Processing Conference (EUSIPCO)
Antal sider5
ForlagIEEE
Publikationsdato2018
Artikelnummer8553039
ISBN (Trykt)978-90-827970-0-8
ISBN (Elektronisk)978-9-0827-9701-5
DOI
StatusUdgivet - 2018
Begivenhed26th European Signal Processing Conference (EUSIPCO 2018) - Rome, Italien
Varighed: 3 sep. 20187 sep. 2018
Konferencens nummer: 26
http://www.eusipco2018.org

Konference

Konference26th European Signal Processing Conference (EUSIPCO 2018)
Nummer26
LandItalien
ByRome
Periode03/09/201807/09/2018
Internetadresse
NavnProceedings of the European Signal Processing Conference
ISSN2076-1465

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Speech enhancement
Factorization
Power spectral density

Citer dette

Kavalekalam, M. S., Nielsen, J. K., Shi, L., Christensen, M. G., & Boldt, J. (2018). Online Parametric NMF for Speech Enhancement. I 2018 26th European Signal Processing Conference (EUSIPCO) [8553039] IEEE. Proceedings of the European Signal Processing Conference https://doi.org/10.23919/EUSIPCO.2018.8553039
Kavalekalam, Mathew Shaji ; Nielsen, Jesper Kjær ; Shi, Liming ; Christensen, Mads Græsbøll ; Boldt, Jesper. / Online Parametric NMF for Speech Enhancement. 2018 26th European Signal Processing Conference (EUSIPCO). IEEE, 2018. (Proceedings of the European Signal Processing Conference).
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title = "Online Parametric NMF for Speech Enhancement",
abstract = "In this paper, we propose a speech enhancement method based on non-negative matrix factorization (NMF) techniques. NMF techniques allow us to approximate the power spectral density (PSD) of the noisy signal as a weighted linear combination of trained speech and noise basis vectors arranged as the columns of a matrix. In this work, we propose to use basis vectors that are parameterised by autoregressive (AR) coefficients. Parametric representation of the spectral basis is beneficial as it can encompass the signal characteristics like, e.g. the speech production model. It is observed that the parametric representation of basis vectors is beneficial while performing online speech enhancement in low delay scenarios.",
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Kavalekalam, MS, Nielsen, JK, Shi, L, Christensen, MG & Boldt, J 2018, Online Parametric NMF for Speech Enhancement. i 2018 26th European Signal Processing Conference (EUSIPCO)., 8553039, IEEE, Proceedings of the European Signal Processing Conference, Rome, Italien, 03/09/2018. https://doi.org/10.23919/EUSIPCO.2018.8553039

Online Parametric NMF for Speech Enhancement. / Kavalekalam, Mathew Shaji; Nielsen, Jesper Kjær; Shi, Liming; Christensen, Mads Græsbøll; Boldt, Jesper.

2018 26th European Signal Processing Conference (EUSIPCO). IEEE, 2018. 8553039 (Proceedings of the European Signal Processing Conference).

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

TY - GEN

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AU - Kavalekalam, Mathew Shaji

AU - Nielsen, Jesper Kjær

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AU - Christensen, Mads Græsbøll

AU - Boldt, Jesper

PY - 2018

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N2 - In this paper, we propose a speech enhancement method based on non-negative matrix factorization (NMF) techniques. NMF techniques allow us to approximate the power spectral density (PSD) of the noisy signal as a weighted linear combination of trained speech and noise basis vectors arranged as the columns of a matrix. In this work, we propose to use basis vectors that are parameterised by autoregressive (AR) coefficients. Parametric representation of the spectral basis is beneficial as it can encompass the signal characteristics like, e.g. the speech production model. It is observed that the parametric representation of basis vectors is beneficial while performing online speech enhancement in low delay scenarios.

AB - In this paper, we propose a speech enhancement method based on non-negative matrix factorization (NMF) techniques. NMF techniques allow us to approximate the power spectral density (PSD) of the noisy signal as a weighted linear combination of trained speech and noise basis vectors arranged as the columns of a matrix. In this work, we propose to use basis vectors that are parameterised by autoregressive (AR) coefficients. Parametric representation of the spectral basis is beneficial as it can encompass the signal characteristics like, e.g. the speech production model. It is observed that the parametric representation of basis vectors is beneficial while performing online speech enhancement in low delay scenarios.

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DO - 10.23919/EUSIPCO.2018.8553039

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Kavalekalam MS, Nielsen JK, Shi L, Christensen MG, Boldt J. Online Parametric NMF for Speech Enhancement. I 2018 26th European Signal Processing Conference (EUSIPCO). IEEE. 2018. 8553039. (Proceedings of the European Signal Processing Conference). https://doi.org/10.23919/EUSIPCO.2018.8553039