Speech enhancement with an adaptive Wiener filter

  • Authors:

  • Marwa A. Abd El-Fattah;Moawad I. Dessouky;Alaa M. Abbas;Salaheldin M. Diab;El-Sayed M. El-Rabaie;Waleed Al-Nuaimy;Saleh A. Alshebeili;Fathi E. Abd El-Samie

  • Affiliations:

  • Department of Electronics and Electrical Communications, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt 32952;Department of Electronics and Electrical Communications, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt 32952;Department of Electronics and Electrical Communications, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt 32952;Department of Electronics and Electrical Communications, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt 32952;Department of Electronics and Electrical Communications, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt 32952;Department of Electrical Engineering and Electronics, The University of Liverpool, Liverpool, UK L69 3GJ;Electrical Engineering Department, KACST-TIC in Radio Frequency and Photonics for the e-Society (RFTONICS), King Saud University, Riyadh, Kingdom of Saudi Arabia;Department of Electronics and Electrical Communications, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt 32952 and KACST-TIC in Radio Frequency and Photonics for the e-Societ ...

  • Venue:
  • International Journal of Speech Technology
  • Year:
  • 2014

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Abstract

This paper proposes an adaptive Wiener filtering method for speech enhancement. This method depends on the adaptation of the filter transfer function from sample to sample based on the speech signal statistics; the local mean and the local variance. It is implemented in the time domain rather than in the frequency domain to accommodate for the time-varying nature of the speech signals. The proposed method is compared to the traditional frequency-domain Wiener filtering, spectral subtraction and wavelet denoising methods using different speech quality metrics. The simulation results reveal the superiority of the proposed Wiener filtering method in the case of Additive White Gaussian Noise (AWGN) as well as colored noise.