Audio Declipping with (Weighted) Analysis Social Sparsity
TL
TL;DR
This work develops the analysis (cosparse) variant of the popular audio declipping algorithm and extends both the old and the new variants by the possibility of weighting the time-frequency coefficients, finding the proposed analysis variant incorporating PEW slightly outperforms the synthesis counterpart in terms of an auditorily motivated metric.
Abstract
We develop the analysis (cosparse) variant of the popular audio declipping algorithm of Siedenburg et al. (2014). Furthermore, we extend both the old and the new variants by the possibility of weighting the time-frequency coefficients. We examine the audio reconstruction performance of several combinations of weights and shrinkage operators. The weights are shown to improve the reconstruction quality in some cases; however, the best scores achieved by the non-weighted methods are not surpassed with the help of weights. Yet, the analysis Empirical Wiener (EW) shrinkage was able to reach the quality of a computationally more expensive competitor, the Persis-tent Empirical Wiener (PEW). Moreover, the proposed analysis variant incorporating PEW slightly outperforms the synthesis counterpart in terms of an auditorily motivated metric.
References28 items
1
APPLADE: Adjustable Plug-and-Play Audio Declipper Combining DNN with Sparse Optimization
2
Audio declipping performance enhancement via crossfading
3
Audio Dequantization Using (Co)Sparse (Non)Convex Methods
4
A Survey and an Extensive Evaluation of Popular Audio Declipping Methods
5