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