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In this paper, we present a scheme for extending deep neural network-basedmultiplicative maskers to deep subband filters for speech restoration in thetime-frequency domain. The resulting method can be generically applied to anydeep neural network providing masks in the time-frequency domain, whilerequiring only few more trainable parameters and a computational overhead thatis negligible for state-of-the-art neural networks. We demonstrate that theresulting deep subband filtering scheme outperforms multiplicative masking fordereverberation, while leaving the denoising performance virtually the same. Weargue that this is because deep subband filtering in the time-frequency domainfits the subband approximation often assumed in the dereverberation literature,whereas multiplicative masking corresponds to the narrowband approximationgenerally employed for denoising.

Extending DNN-based Multiplicative Masking to Deep Subband Filtering for Improved Dereverberation