InforLorV4, Main, Exploration, bibRecord, 000555

Blind suppression of nonstationary diffuse noise based on spatial covariance matrix decomposition

Identifieur interne : 000555 ( Main/Exploration ); précédent : 000554; suivant : 000556

Blind suppression of nonstationary diffuse noise based on spatial covariance matrix decomposition

Auteurs : Nobutaka Ito [Japon] ; Emmanuel Vincent [France] ; Tomohiro Nakatani [Japon] ; Nobutaka Ono [Japon] ; Shoko Araki [Japon] ; Shigeki Sagayama [Japon]

Source :

Journal of Signal Processing Systems [ 1939-8018 ] ; 2015-05-01.

RBID : Hal:hal-01020255

Abstract

We propose methods for blind suppression of nonstationary diffuse noise based on decomposition of the observed spatial covariance matrix into signal and noise parts. In modeling noise to regularize the ill-posed decomposition problem, we exploit spatial invariance (isotropy) instead of temporal invariance (stationarity). The isotropy assumption is that the spatial cross-spectrum of noise is dependent on the distance between microphones and independent of the direction between them. We propose methods for spatial covariance matrix decomposition based on least squares and maximum likelihood estimation. The methods are validated on real-world recordings.

Url:

https://hal.inria.fr/hal-01020255

Affiliations:

Links toward previous steps (curation, corpus...)

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Le document en format XML

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<front><div type="abstract" xml:lang="en">We propose methods for blind suppression of nonstationary diffuse noise based on decomposition of the observed spatial covariance matrix into signal and noise parts. In modeling noise to regularize the ill-posed decomposition problem, we exploit spatial invariance (isotropy) instead of temporal invariance (stationarity). The isotropy assumption is that the spatial cross-spectrum of noise is dependent on the distance between microphones and independent of the direction between them. We propose methods for spatial covariance matrix decomposition based on least squares and maximum likelihood estimation. The methods are validated on real-world recordings.</div>
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Blind suppression of nonstationary diffuse noise based on spatial covariance matrix decomposition

Blind suppression of nonstationary diffuse noise based on spatial covariance matrix decomposition

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