  Single-channel speech enhancement with deep neural networks (DNNs) has shown
promising performance and is thus intensively being studied. In this paper,
instead of applying the mean squared error (MSE) as the loss function during
DNN training for speech enhancement, we design a perceptual weighting filter
loss motivated by the weighting filter as it is employed in
analysis-by-synthesis speech coding, e.g., in code-excited linear prediction
(CELP). The experimental results show that the proposed simple loss function
improves the speech enhancement performance compared to a reference DNN with
MSE loss in terms of perceptual quality and noise attenuation. The proposed
loss function can be advantageously applied to an existing DNN-based speech
enhancement system, without modification of the DNN topology for speech
enhancement. The source code for the proposed approach is made available.
