Variance normalized perceptual subspace speech enhancement with noise estimation using SPP

In this paper, a perceptual subspace speech enhancement method using masking properties of human auditory system is proposed. Noise was estimated at every frame using Speech Presence Probability (SPP). Variance normalization was further done to remove the abrupt changes in the noisy speech signal, removing noise. A combined masking threshold is calculated from simultaneous and temporal masking, which is used to decide the gain parameters for the algorithm. Spectral Domain Constrained (SDC) estimator was employed in determining the filter coefficients. The performance evaluation was done using wcep and WSS objective measures and subjective informal listening test. The results show a superior performance of the proposed method over some of the existing speech enhancement methods.