Audio declipping performance enhancement via crossfading

Abstract

Some audio declipping methods produce waveforms that do not fully respect the actual process of clipping and allow a deviation on the reliable samples. This article reports what effect on perception it has if the output of such “inconsistent” methods is pushed towards “consistent” solutions by postprocessing. We first propose a simple sample replacement method, then we identify its main weaknesses and propose an improved variant. The experiments show that the vast majority of inconsistent declipping methods significantly benefit from the proposed approach in terms of objective perceptual metrics. In particular, we show that the SS PEW method based on social sparsity combined with the proposed method performs comparable to top methods from the consistent class, but at a computational cost of one order of magnitude lower.Some audio declipping methods produce waveforms that do not fully respect the actual process of clipping and allow a deviation on the reliable samples. This article reports what effect on perception it has if the output of such “inconsistent” methods is pushed towards “consistent” solutions by postprocessing. We first propose a simple sample replacement method, then we identify its main weaknesses and propose an improved variant. The experiments show that the vast majority of inconsistent declipping methods significantly benefit from the proposed approach in terms of objective perceptual metrics. In particular, we show that the SS PEW method based on social sparsity combined with the proposed method performs comparable to top methods from the consistent class, but at a computational cost of one order of magnitude lower.