  This study proposes a fully convolutional network (FCN) model for raw
waveform-based speech enhancement. The proposed system performs speech
enhancement in an end-to-end (i.e., waveform-in and waveform-out) manner, which
dif-fers from most existing denoising methods that process the magnitude
spectrum (e.g., log power spectrum (LPS)) only. Because the fully connected
layers, which are involved in deep neural networks (DNN) and convolutional
neural networks (CNN), may not accurately characterize the local information of
speech signals, particularly with high frequency components, we employed fully
convolutional layers to model the waveform. More specifically, FCN consists of
only convolutional layers and thus the local temporal structures of speech
signals can be efficiently and effectively preserved with relatively few
weights. Experimental results show that DNN- and CNN-based models have limited
capability to restore high frequency components of waveforms, thus leading to
decreased intelligibility of enhanced speech. By contrast, the proposed FCN
model can not only effectively recover the waveforms but also outperform the
LPS-based DNN baseline in terms of short-time objective intelligibility (STOI)
and perceptual evaluation of speech quality (PESQ). In addition, the number of
model parameters in FCN is approximately only 0.2% compared with that in both
DNN and CNN.
