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

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• 作者：Nobutaka Ito (1)
  Emmanuel Vincent (2)
  Tomohiro Nakatani (1)
  Nobutaka Ono (3) (4)
  Shoko Araki (1)
  Shigeki Sagayama (5)
  
  1. NTT Communication Science Laboratories ; NTT Corporation ; Kyoto ; Japan
  2. Inria ; Nancy ; France
  3. Principles of Informatics Research Division ; National Institute of Informatics ; Tokyo ; Japan
  4. School of Multidisciplinary Sciences ; The Graduate University for Advanced Studies (SOKENDAI) ; Tokyo ; Japan
  5. School of Interdisciplinary Mathematical Sciences ; Meiji University ; Tokyo ; Japan
  
• 关键词：Noise suppression ; Diffuse noise ; Spatial covariance matrix ; Maximum likelihood estimation ; Least squares estimation
• 刊名：The Journal of VLSI Signal Processing
• 出版年：2015
• 出版时间：May 2015
• 年：2015
• 卷：79
• 期：2
• 页码：145-157
• 全文大小：2,259 KB
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• 刊物类别：Engineering
• 刊物主题：Electrical Engineering
  Circuits and Systems
  Computer Imaging, Vision, Pattern Recognition and Graphics
  Computer Systems Organization and Communication Networks
  Signal,Image and Speech Processing
  Mathematics of Computing
  
• 出版者：Springer New York
• ISSN：1939-8115

文摘

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.