Structured sparsity for audio signals

Kai Siedenburg; Monika Dörfler
DAFx-2011 - Paris
Regression problems with mixed-norm priors on time-frequency coefficients lead to structured, sparse representations of audio signals. In this contribution, a systematic formulation of thresholding operators that allow for weighting in the time-frequency domain is presented. The related iterative algorithms are then evaluated on synthetic and real-life audio signals in the context of denoising and multi-layer decomposition. Further, initial results on the influence of the shape of the weighting masks are presented.