Estimators for speech enhancement by using
wavelet transform is the new technique which is proposed in
this paper. Here, we proposed a new set of estimators called
magnitude square spectrum estimators beyond the
conventional magnitude, power estimators using wavelet
transform. Maximum a posteriori(MAP), Minimum Mean
Square Error(MMSE) Estimators are derived using hard
masking then Soft Masking by Incorporating a Posterior
SNR uncertainty (SMPO),Soft Masking by Incorporating a
Priori SNR uncertainty(SMPR) Estimators are derived
using soft masking with wavelet transformations. These
estimators are evaluated using the parameters Mean Square
Error (MSE), Perceptual Evaluation of Speech quality
(PESQ), Signal to Noise Ratio(SNR). The results showed that
these magnitude square spectrum estimators reduces the
distortion because of the localization property of the wavelet
transform and increases the quality and signal strength of
the speech signals.
Published In : IJCAT Journal Volume 2, Issue 7
Date of Publication : July 2015
Pages : 219 - 226
Tables : 06
Publication Link :Speech Enhancement using Statistical Estimators
Based on Wavelet Transformations
V.VIJAYA LAKSHMI : received the B.Tech
degree in electronics and communication
engineering in 2013 from Amrita Sai Institute of
Science and Technology, M.tech pursuing Digital
Electronics and Communication Systems in
Gudlavalleru engineering college.
Dr. V.V.K.D.V. Prasad : presently working
Professor of ECE in Gudlavalleru engineering
college from 2013 to till date. Current area of
research in denoising of signals and images using
wavelets transforms and S transforms.
From the results, it is proved that the magnitude
square spectum estimators with wavelet transforms
increases the Speech quality(PESQ),signal to noise
ratio(SNR) and decreases the Mean square
error(MSE).Out of all the five estimators derived it is
observed that SMPO(soft masking incorporating
posteriori SNR) shows the best performance.
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