Blind Suppression of Nonstationary Diffuse Acoustic Noise Based on Spatial Covariance Matrix Decomposition

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 inva...

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Vydáno v:	Journal of Signal Processing Systems Ročník 79; číslo 2; s. 145 - 157
Hlavní autoři:	Ito, Nobutaka, Vincent, Emmanuel, Nakatani, Tomohiro, Ono, Nobutaka, Araki, Shoko, Sagayama, Shigeki
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Boston Springer Science and Business Media LLC 01.05.2015 Springer US
Témata:	[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing Circuits and Systems Computer Imaging Diffuse noise Electrical Engineering Engineering Image Processing and Computer Vision Least squares estimation Maximum likelihood estimation Noise suppression Pattern Recognition Pattern Recognition and Graphics Signal,Image and Speech Processing Spatial covariance matrix Vision Diffuse noise Maximum likelihood estimation Spatial covariance matrix Least squares estimation Noise suppression
ISSN:	1939-8018, 1939-8115
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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 data.
AbstractList	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 data.
Author	Araki, Shoko Sagayama, Shigeki Vincent, Emmanuel Nakatani, Tomohiro Ito, Nobutaka Ono, Nobutaka
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Cites_doi	10.1109/TASSP.1979.1163209 10.1109/78.942614 10.1109/TSP.2002.801937 10.1016/0167-6393(90)90011-W 10.1109/TASSP.1984.1164453 10.1109/MSP.2013.2297440 10.1109/MLSP.2013.6661984 10.1109/ICASSP.1988.197172 10.1007/978-3-662-04619-7 10.1109/ASPAA.2011.6082320 10.1111/j.2517-6161.1977.tb01600.x 10.1007/978-3-642-15995-4_11
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Contributor	Vincent, Emmanuel National Institute of Informatics (NII) Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH) ; Inria Nancy - Grand Est ; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD) ; Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA) ; Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA) ; Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)
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SubjectTerms	[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing Circuits and Systems Computer Imaging Diffuse noise Electrical Engineering Engineering Image Processing and Computer Vision Least squares estimation Maximum likelihood estimation Noise suppression Pattern Recognition Pattern Recognition and Graphics Signal,Image and Speech Processing Spatial covariance matrix Vision
Title	Blind Suppression of Nonstationary Diffuse Acoustic Noise Based on Spatial Covariance Matrix Decomposition
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