Perceptually based speech enhancement using the weighted β-SA estimator

In this paper, we first propose a new family of Bayesian estimators for speech enhancement where the cost function includes both a power law and a weighting factor. Secondly, we set the parameters of the estimator based on perceptual considerations by taking into account the masking properties of the ear and the perceived loudness of sound. Our results show that the new estimator achieves better overall performance than existing Bayesian estimators both in terms of objective and subjective measures. Specifically, it shows a segmental SNR improvement of up to 0.65 dB while it obtains the best scores in a MUSHRA test for both white and aircraft cockpit noises.