Psychoacoustically-motivated adaptive β-order generalized spectral subtraction based on data-driven optimization

To mitigate the performance limitations caused by the constant spectral order β in the traditional spectral subtraction methods, we previously presented an adaptive β-order generalized spectral subtraction (GSS) in which the spectral order β is updated in a heuristic way [10]. In this paper, we propose a psychoacoustically-motivated adaptive β-order GSS, by considering that different frequency bands contribute different amounts to speech intelligibility (i.e., the bandimportance function). Specifically, in this proposed adaptive β-order GSS, the tendency of spectral order β to change with the input local signal-to-noise ratio (SNR) is quantitatively approximated by a sigmoid function, which is derived through a data-driven optimization procedure by minimizing the intelligibility-weighted distance between the desired speech spectrum and its estimate. The inherent parameters of the sigmoid function are further optimized with the data-driven optimization procedure. Experimental results indicate that the proposed psychoacoustically-motivated adaptive β-order GSS yields great improvements over the traditional spectral subtraction methods with the intelligibility-weighted measures.