Speech Enhancement using Statistical Estimators Based on Wavelet Transformations
نویسندگان
چکیده
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.
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