将基于深度神经网络的可乘掩蔽扩展到深度子带滤波以改进去混响
Extending DNN-based Multiplicative Masking to Deep Subband Filtering for Improved Dereverberation
AI总结：
本文将深度神经网络的可乘掩蔽扩展为深度子带滤波，以极少的额外参数和计算开销提升去混响性能，同时保持去噪性能基本不变。
摘要：
本文提出一种方案，将基于深度神经网络的可乘掩蔽器扩展为深度子带滤波器，用于时频域中的语音恢复。所得到的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，同时只需增加极少的可训练参数，并且其计算开销对于最先进的神经网络而言可以忽略不计。我们证明，所得到的深度子带滤波方案在去混响方面优于可乘掩蔽，同时去噪性能几乎保持不变。我们认为，这是因为时频域中的深度子带滤波符合去混响文献中通常假设的子带近似，而可乘掩蔽对应于通常用于去噪的窄带近似。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
