23 research outputs found
Sparsity and cosparsity for audio declipping: a flexible non-convex approach
This work investigates the empirical performance of the sparse synthesis
versus sparse analysis regularization for the ill-posed inverse problem of
audio declipping. We develop a versatile non-convex heuristics which can be
readily used with both data models. Based on this algorithm, we report that, in
most cases, the two models perform almost similarly in terms of signal
enhancement. However, the analysis version is shown to be amenable for real
time audio processing, when certain analysis operators are considered. Both
versions outperform state-of-the-art methods in the field, especially for the
severely saturated signals
Sparse and Cosparse Audio Dequantization Using Convex Optimization
The paper shows the potential of sparsity-based methods in restoring
quantized signals. Following up on the study of Brauer et al. (IEEE ICASSP
2016), we significantly extend the range of the evaluation scenarios: we
introduce the analysis (cosparse) model, we use more effective algorithms, we
experiment with another time-frequency transform. The paper shows that the
analysis-based model performs comparably to the synthesis-model, but the Gabor
transform produces better results than the originally used cosine transform.
Last but not least, we provide codes and data in a reproducible way
Audio Declipping with Social Sparsity
International audienceWe 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 \emph{windowed group-Lasso} and the \emph{persistent 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
Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)
The implicit objective of the biennial "international - Traveling Workshop on
Interactions between Sparse models and Technology" (iTWIST) is to foster
collaboration between international scientific teams by disseminating ideas
through both specific oral/poster presentations and free discussions. For its
second edition, the iTWIST workshop took place in the medieval and picturesque
town of Namur in Belgium, from Wednesday August 27th till Friday August 29th,
2014. The workshop was conveniently located in "The Arsenal" building within
walking distance of both hotels and town center. iTWIST'14 has gathered about
70 international participants and has featured 9 invited talks, 10 oral
presentations, and 14 posters on the following themes, all related to the
theory, application and generalization of the "sparsity paradigm":
Sparsity-driven data sensing and processing; Union of low dimensional
subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph
sensing/processing; Blind inverse problems and dictionary learning; Sparsity
and computational neuroscience; Information theory, geometry and randomness;
Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?;
Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website:
http://sites.google.com/site/itwist1
Audio declipping performance enhancement via crossfading
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
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
Audio Dequantization Using (Co)Sparse (Non)Convex Methods
The paper deals with the hitherto neglected topic of audio dequantization. It reviews the state-of-the-art sparsity-based approaches and proposes several new methods. Convex as well as non-convex approaches are included, and all the presented formulations come in both the synthesis and analysis variants. In the experiments the methods are evaluated using the signal-to-distortion ratio (SDR) and PEMO-Q, a perceptually motivated metric