7 research outputs found
Revisiting Synthesis Model of Sparse Audio Declipper
The state of the art in audio declipping has currently been achieved by SPADE
(SParse Audio DEclipper) algorithm by Kiti\'c et al. Until now, the
synthesis/sparse variant, S-SPADE, has been considered significantly slower
than its analysis/cosparse counterpart, A-SPADE. It turns out that the opposite
is true: by exploiting a recent projection lemma, individual iterations of both
algorithms can be made equally computationally expensive, while S-SPADE tends
to require considerably fewer iterations to converge. In this paper, the two
algorithms are compared across a range of parameters such as the window length,
window overlap and redundancy of the transform. The experiments show that
although S-SPADE typically converges faster, the average performance in terms
of restoration quality is not superior to A-SPADE
Introducing SPAIN (SParse Audio INpainter)
A novel sparsity-based algorithm for audio inpainting is proposed. It is an
adaptation of the SPADE algorithm by Kiti\'c et al., originally developed for
audio declipping, to the task of audio inpainting. The new SPAIN (SParse Audio
INpainter) comes in synthesis and analysis variants. Experiments show that both
A-SPAIN and S-SPAIN outperform other sparsity-based inpainting algorithms.
Moreover, A-SPAIN performs on a par with the state-of-the-art method based on
linear prediction in terms of the SNR, and, for larger gaps, SPAIN is even
slightly better in terms of the PEMO-Q psychoacoustic criterion
A new generalized projection and its application to acceleration of audio declipping
In convex optimization, it is often inevitable to work with projectors onto convex sets composed with a linear operator. Such a need arises from both the theory and applications, with signal processing being a prominent and broad field where convex optimization has been used recently. In this article, a novel projector is presented, which generalizes previous results in that it admits to work with a broader family of linear transforms when compared with the state of the art but, on the other hand, it is limited to box-type convex sets in the transformed domain. The new projector is described by an explicit formula, which makes it simple to implement and requires a low computational cost. The projector is interpreted within the framework of the so-called proximal splitting theory. The convenience of the new projector is demonstrated on an example from signal processing, where it was possible to speed up the convergence of a signal declipping algorithm by a factor of more than two