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In this paper, we propose an audio declipping method that takes advantages ofboth sparse optimization and deep learning. Since sparsity-based audiodeclipping methods have been developed upon constrained optimization, they areadjustable and well-studied in theory. However, they always uniformly promotesparsity and ignore the individual properties of a signal. Deep neural network(DNN)-based methods can learn the properties of target signals and use them foraudio declipping. Still, they cannot perform well if the training data havemismatches and/or constraints in the time domain are not imposed. In theproposed method, we use a DNN in an optimization algorithm. It is inspired byan idea called plug-and-play (PnP) and enables us to promote sparsity based onthe learned information of data, considering constraints in the time domain.Our experiments confirmed that the proposed method is stable and robust tomismatches between training and test data.

APPLADE: Adjustable Plug-and-play Audio Declipper Combining DNN with Sparse Optimization