Abstract

In this paper we describe a method for enhancing speech that is corrupted by additive background noise varying in time. The proposed nonlinear spectral-subtraction approach is based on sequentially updating the estimated noise per frame and adapting a masking property of the human ear. This speech enhancement method aims to decrease noise perception on the speech voice segments, based on sequential noise estimation that follows changes in the background noise. Furthermore, to enhance speech elements obtained by using the masking effect, we developed an adaptive control of a scaling function, calculated by the regression line using the noise-masking signal-tonoise ratio (NMSNR). Experimental results show that the proposed approach can efficiently remove additive noise related to various types of noise corruption.

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