27 research outputs found
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
A Proper version of Synthesis-based Sparse Audio Declipper
Methods based on sparse representation have found great use in the recovery
of audio signals degraded by clipping. The state of the art in declipping has
been achieved by the SPADE algorithm by Kiti\'c et. al. (LVA/ICA2015). Our
recent study (LVA/ICA2018) has shown that although the original S-SPADE can be
improved such that it converges significantly faster than the A-SPADE, the
restoration quality is significantly worse. In the present paper, we propose a
new version of S-SPADE. Experiments show that the novel version of S-SPADE
outperforms its old version in terms of restoration quality, and that it is
comparable with the A-SPADE while being even slightly faster than A-SPADE
Multiple Hankel matrix rank minimization for audio inpainting
Sasaki et al. (2018) presented an efficient audio declipping algorithm, based
on the properties of Hankel-structured matrices constructed from time-domain
signal blocks. We adapt their approach to solve the audio inpainting problem,
where samples are missing in the signal. We analyze the algorithm and provide
modifications, some of them leading to an improved performance. Overall, it
turns out that the new algorithms perform reasonably well for speech signals
but they are not competitive in the case of music signals
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