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 in denoising.

翻译：本文提出了一种方案，将基于深度神经网络的多重掩蔽器扩展为用于语音恢复的时频域深子带滤波器。该方法可通用地应用于任何在时频域提供掩蔽的深度神经网络，且仅需增加少量可训练参数和可忽略的额外计算开销。我们证明，与多重掩蔽相比，该深子带滤波方案在去混响方面表现更优，而去噪性能几乎保持不变。我们认为这是因为时频域深子带滤波符合去混响文献中常采用的子带近似假设，而多重掩蔽则对应于去噪中普遍使用的窄带近似。