15 research outputs found

    Multiple Hankel matrix rank minimization for audio inpainting

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    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

    Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)

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    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

    Revisiting Synthesis Model of Sparse Audio Declipper

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    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

    A new generalized projection and its application to acceleration of audio declipping

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    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

    mathematical modeling of human behavior in video image

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    研究成果の概要 (和文) : 本研究では、防犯カメラ等で観測された人物行動を裁判の証拠として活用する手法について取り組んだ。防犯カメラ映像に映った人物の間接部位のうち、障害物に隠れて位置が計測できない部位の位置を推定する手法を導出した。また、人物行動を表現する数学モデル構築について取り組み、複数の線形システムの重み付き平均により、人物行動を表現する数学モデルを構築した。研究成果の概要 (英文) : This work provided a estimation method for unoberved human behavior in video image and proposed a new mathematical model to describe human behavior in video image by a weighted combination of linear systems

    Reconstruction de phase et de signaux audio avec des fonctions de coût non-quadratiques

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    Audio signal reconstruction consists in recovering sound signals from incomplete or degraded representations. This problem can be cast as an inverse problem. Such problems are frequently tackled with the help of optimization or machine learning strategies. In this thesis, we propose to change the cost function in inverse problems related to audio signal reconstruction. We mainly address the phase retrieval problem, which is common when manipulating audio spectrograms. A first line of work tackles the optimization of non-quadratic cost functions for phase retrieval. We study this problem in two contexts: audio signal reconstruction from a single spectrogram and source separation. We introduce a novel formulation of the problem with Bregman divergences, as well as algorithms for its resolution. A second line of work proposes to learn the cost function from a given dataset. This is done under the framework of unfolded neural networks, which are derived from iterative algorithms. We introduce a neural network based on the unfolding of the Alternating Direction Method of Multipliers, that includes learnable activation functions. We expose the relation between the learning of its parameters and the learning of the cost function for phase retrieval. We conduct numerical experiments for each of the proposed methods to evaluate their performance and their potential with audio signal reconstruction
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