Extending DNN-based Multiplicative Masking to Deep Subband Filtering for Improved Dereverberation
This work addresses speech restoration for applications like hearing aids or communication systems, but it is incremental as it builds on existing DNN masking methods.
The paper tackles speech dereverberation by extending DNN-based multiplicative masking to deep subband filtering, resulting in improved performance over multiplicative masking while maintaining similar denoising capabilities with minimal added parameters and computational cost.
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.