Audio declipping with social sparsity

Kai Siedenburg, Matthieu Kowalski, Monika Dorfler
2014 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)  
We consider the audio declipping problem by using iterative thresholding algorithms and the principle of social sparsity. This recently introduced approach features thresholding/shrinkage operators which allow to model dependencies between neighboring coefficients in expansions with time-frequency dictionaries. A new unconstrained convex formulation of the audio declipping problem is introduced. The chosen structured thresholding operators are the so called windowed group-Lasso and the
more » ... t empirical Wiener. The usage of these operators significantly improves the quality of the reconstruction, compared to simple soft-thresholding. The resulting algorithm is fast, simple to implement, and it outperforms the state of the art in terms of signal to noise ratio.
doi:10.1109/icassp.2014.6853863 dblp:conf/icassp/SiedenburgKD14 fatcat:bc4dv5xszvfw7l3babrjotbelu