用于语音增强的可训练自适应窗切换
Trainable Adaptive Window Switching for Speech Enhancement
摘要：
本文提出了一种可训练的自适应窗口切换（AWS）方法，并将其应用于深度神经网络（DNN），在改进的离散余弦变换域中进行语音增强。短时傅里叶变换域的时频掩蔽处理是一种典型的语音增强方法。为了精确地恢复目标信号，最近已经研究并使用基于DNN的短时频率变换来代替STFT。然而，由于这种固定分辨率的短时频率变换方法具有基于不确定性原理的T-F分辨率问题，因此不仅短时频率变换而且加窗函数的长度都应该被优化。为了克服这个问题，我们将AWS纳入到语音增强过程中，并且根据输入信号使用DNN来操纵每个时间帧的窗口函数。我们证实，所提出的方法实现了更高的信号失真比比传统的语音增强方法在固定分辨率的频域。
英文摘要：
This study proposes a trainable adaptive window switching (AWS) method and apply it to a deep-neural-network (DNN) for speech enhancement in the modified discrete cosine transform domain. Time-frequency (T-F) mask processing in the short-time Fourier transform (STFT)-domain is a typical speech enhancement method. To recover the target signal precisely, DNN-based short-time frequency transforms have recently been investigated and used instead of the STFT. However, since such a fixed-resolution short-time frequency transform method has a T-F resolution problem based on the uncertainty principle, not only the short-time frequency transform but also the length of the windowing function should be optimized. To overcome this problem, we incorporate AWS into the speech enhancement procedure, and the windowing function of each time-frame is manipulated using a DNN depending on the input signal. We confirmed that the proposed method achieved a higher signal-to-distortion ratio than conventional speech enhancement methods in fixed-resolution frequency domains.
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