22 research outputs found
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
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
Algorithms for audio inpainting based on probabilistic nonnegative matrix factorization
International audienceAudio inpainting, i.e., the task of restoring missing or occluded audio signal samples, usually relies on sparse representations or autoregressive modeling. In this paper, we propose to structure the spectrogram with nonnegative matrix factorization (NMF) in a probabilistic framework. First, we treat the missing samples as latent variables, and derive two expectation-maximization algorithms for estimating the parameters of the model, depending on whether we formulate the problem in the time-or time-frequency domain. Then, we treat the missing samples as parameters, and we address this novel problem by deriving an alternating minimization scheme. We assess the potential of these algorithms for the task of restoring short-to middle-length gaps in music signals. Experiments reveal great convergence properties of the proposed methods, as well as competitive performance when compared to state-of-the-art audio inpainting techniques