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International Journal of Speech Technology

, Volume 21, Issue 4, pp 877–886 | Cite as

Reduction of residual noise based on eigencomponent filtering for speech enhancement

  • Kewen Huang
  • Yimin Liu
  • Yuanquan Hong
Article
  • 20 Downloads

Abstract

In this paper, residual noise of corrupted speech observations is further restrained based on eigencomponent (an eigenvalue and its corresponding eigenvector) filtering. Three relevant algorithms are proposed to obtain the core eigencomponents that deeply affect enhancement quality of speech fragments by joint diagonalization of clean speech and noise covariance matrix. In addition, the generalized inverse matrix transform is introduced to the recovery of enhanced speech signal for the issue of matrix irreversibility after eigencomponents are filtered. Experiment results show that the proposed methods work better than many other methods under various conditions on both noise reduction and speech distortion.

Keywords

Residual noise Covariance matrix Generalized inverse matrix transform Noise reduction Speech distortion 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Physics and MechanicsShaoguan UniversityShaoguanChina

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