  In this paper, a neural network-augmented algorithm for noise-robust online
dereverberation with a Kalman filtering variant of the weighted prediction
error (WPE) method is proposed. The filter stochastic variations are predicted
by a deep neural network (DNN) trained end-to-end using the filter residual
error and signal characteristics. The presented framework allows for robust
dereverberation on a single-channel noisy reverberant dataset similar to
WHAMR!. The Kalman filtering WPE introduces distortions in the enhanced signal
when predicting the filter variations from the residual error only, if the
target speech power spectral density is not perfectly known and the observation
is noisy. The proposed approach avoids these distortions by correcting the
filter variations estimation in a data-driven way, increasing the robustness of
the method to noisy scenarios. Furthermore, it yields a strong dereverberation
and denoising performance compared to a DNN-supported recursive least squares
variant of WPE, especially for highly noisy inputs.
