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Clipping, as a current nonlinear distortion, often occurs due to the limiteddynamic range of audio recorders. It degrades the speech quality andintelligibility and adversely affects the performances of speech and speakerrecognitions. In this paper, we focus on enhancement of clipped speech by usinga fully convolutional neural network as U-Net. Motivated by the idea ofimage-to-image translation, we propose a declipping approach, namely U-Netdeclipper in which the magnitude spectrum images of clipped signals aretranslated to the corresponding images of clean ones. The experimental resultsshow that the proposed approach outperforms other declipping methods in termsof both quality and intelligibility measures, especially in severe clippingcases. Moreover, the superior performance of the U-Net declipper over thewell-known declipping methods is verified in additive Gaussian noiseconditions.

Image to Image Translation based on Convolutional Neural Network Approach for Speech Declipping