Speech enhancement using pre-image iterations

In this paper, we present a new method to de-noise speech in the complex spectral domain. The method is derived from kernel principal component analysis (kPCA). Instead of applying PCA in a high-dimensional feature space and then going back to the original input space by using a solution to the pre-image problem, only the pre-image step is applied for de-noising. We show that the de-noised audio sample is a convex combination of the noisy input data and that the resulting algorithm is closely related to the soft k-means algorithm. Compared to kPCA, this method reduces the computational costs while the audio quality is similar and speech quality measures do not degrade.