Noise reduction based on adaptive β-order generalized spectral subtraction for speech enhancement

Though spectral subtraction has widely been used for speech enhancement, the spectral order β set in spectral subtraction is generally fixed to some constants, resulting in the performance limitation to a certain degree. In this paper, we first analyze the performance of the β-order generalized spectral subtraction in terms of the gain function to highlight its dependence on the value of spectral order β. Based on the analysis results and considering the non-uniform effect of real-world noise on speech signal, we further propose an adaptive β-order generalized spectral subtraction in which the spectral order β is adaptively updated according to the signal-to-noise ratio in each critical band frame by frame as in a sigmoid function. Experimental results in various noise conditions illustrate the superiority of the proposed method with regard to the traditional spectral subtraction methods.