Abstract
In this paper, we propose a novel audio declipping method that fuses sparse-optimization-based and deep neural network (DNN)– based methods. The two methods have contrasting characteristics, depending on clipping level. Sparse-optimization-based audio de-clipping can preserve reliable samples, being suitable for precise restoration of small clipping. Besides, DNN-based methods are potent for recovering large clipping thanks to their data-driven approaches. Therefore, if these two methods are properly combined, audio declipping effective for a wide range of clipping levels can be realized. In the proposed method, we use a framework called consensus equilibrium to fuse the above two methods. Our experiments confirmed that the proposed method was superior to both conventional sparse-optimization-based and DNN-based methods.
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