Noise Estimation and Suppression Using Nonlinear Function with a Priori Speech Absence Probability in Speech Enhancement

This paper proposes a noise-biased compensation of minimum statistics (ms) method using a nonlinear function and a priori speech absence probability (sap) for speech enhancement in highly nonstationary noisy environments. the ms method is a well-known technique for noise power estimation in nonstationary noisy environments; however,it tends to bias noise estimation below that of the true noise level. the proposed method is combined with an adaptive parameter based on a sigmoid function and a priori sap for residual noise reduction. additionally,our method uses an autoparameter to control the trade-off between speech distortion and residual noise. we evaluate the estimation of noise power in highly nonstationary and varying noise environments. the improvement can be confirmed in terms of signal-to-noise ratio (snr) and the itakura-saito distortion measure (isdm). � 2016 soojeong lee and gangseong lee.