Generalization of pre-image iterations for speech enhancement

In this paper, we extend the pre-image iteration method for speech de-noising by automatic determination of the kernel variance. The kernel variance needs to be adapted in different noise conditions. In previous work, the signal-to-noise ratio (SNR) was assumed to be known and the kernel variance was pre-defined using a development set. In the proposed method, a function is derived that maps a noise estimate to a potentially good value for the kernel variance. Hence, the SNR is not required to be known. Furthermore, the method is adapted for scenarios with colored noise, where - due to the properties of the noise - a different kernel variance for each frequency leads to better performance. We compare the proposed methods to the original pre-image iteration method and show an increase in performance in terms of the PEASS quality measures.