  Clipping, as a current nonlinear distortion, often occurs due to the limited
dynamic range of audio recorders. It degrades the speech quality and
intelligibility and adversely affects the performances of speech and speaker
recognitions. In this paper, we focus on enhancement of clipped speech by using
a fully convolutional neural network as U-Net. Motivated by the idea of
image-to-image translation, we propose a declipping approach, namely U-Net
declipper in which the magnitude spectrum images of clipped signals are
translated to the corresponding images of clean ones. The experimental results
show that the proposed approach outperforms other declipping methods in terms
of both quality and intelligibility measures, especially in severe clipping
cases. Moreover, the superior performance of the U-Net declipper over the
well-known declipping methods is verified in additive Gaussian noise
conditions.
