Speech signal enhancement based on MAP algorithm in the ICA space

Xin Zou, Peter Jancovic, Ju Liu, M Kokuer

Research output: Contribution to journalArticlepeer-review

35 Citations (Scopus)


This paper presents a novel maximum a posteriori (MAP) denoising algorithm based on the independent component analysis (ICA). We demonstrate that the employment of individual ICA transformations for signal and noise can provide the best estimate within the linear framework. The signal enhancement problem is categorized based on the distribution of signal and noise being Gaussian or non-Gaussian and the estimation rule is derived for each of the categories. Our theoretical analysis shows that under the assumption of a Gaussian noise the proposed algorithm leads to some well-known enhancement techniques, i.e., Wiener filter and sparse code shrinkage. The analysis of the denoising capability shows that the proposed algorithm is most efficient for non-Gaussian signals corrupted by a non-Gaussian noise. We employed the generalized Gaussian model (GGM) to model the distributions of speech and noise. Experimental evaluation is performed in terms of signal-to-noise ratio (SNR) and spectral distortion measure. Experimental results show that the proposed algorithms achieve significant improvement on the enhancement performance in both Gaussian and non-Gaussian noise.
Original languageEnglish
Pages (from-to)1812-1820
Number of pages9
JournalIEEE Transactions on Signal Processing
Issue number5
Publication statusPublished - May 2008


  • maximum a posteriori (MAP) estimation
  • speech enhancement
  • sparse code shrinkage
  • independent component analysis (ICA)
  • Wiener filter
  • non-Gaussian noise
  • generalized Gaussian model


Dive into the research topics of 'Speech signal enhancement based on MAP algorithm in the ICA space'. Together they form a unique fingerprint.

Cite this