IEEE ICASSP 2020 Virtual Conference May 2020

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  • Neural Time Warping For Multiple Sequence Alignment

    00:12:13
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    Multiple sequence alignment (MSA) is a traditional and still challenging task for time-series analyses. The MSA problem is intrinsically a discrete optimization and, in principle, dynamic programming is available for solving MSA. However, the computation
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  • Smoothing Graph Signals Via Random Spanning Forests

    00:14:55
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    Another facet of the elegant link between random processes on graphs and Laplacian-based numerical linear algebra is uncovered: based on random spanning forests, novel Monte-Carlo estimators for graph signal smoothing are proposed. These random forests ar
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  • Large-Scale Time Series Clustering With K-Ars

    00:12:26
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    Time-series clustering involves grouping homogeneous time series together based on certain similarity measures. The mixture AR model (MxAR) has already been developed for time series clustering, as well as an associated EM algorithm. However, this EM clus
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  • The Fractional Quaternion Fourier Number Transform

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    In this paper, we define a fractional version of the quaternion Fourier number transform (QFNT). With this purpose, we first study the eigenstructure of the QFNT; this is used to obtain the eigendecomposition of the corresponding transform matrix, from wh
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  • A Gated Hypernet Decoder For Polar Codes

    00:12:28
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    Hypernetworks were recently shown to improve the performance of message passing algorithms for decoding error correcting codes. In this work, we demonstrate how hypernetworks can be applied to decode polar codes by employing a new formalization of the pol
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We propose a straightforward and cost-effective method to perform diffuse soundfield measurements for calibrating the magnitude response of a microphone array. Typically, such calibration is performed in a diffuse soundfield created in reverberation chamb
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  • Shape From Bandwidth: Central Projection Case

    00:12:03
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    Consider an unknown surface painted with a band-limited texture. We show that only the knowledge of the bandwidth of the texture is enough to estimate the shape of the surface from a single image taken by a camera. We model the problem as a central projec
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  • Batman: Bayesian Target Modelling For Active Inference

    00:12:11
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    Active Inference is an emerging framework for designing intelligent agents. In an Active Inference setting, any task is formulated as a variational free energy minimisation problem on a generative probabilistic model. Goal-directed behaviour relies on a c
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  • Graph Regularized Tensor Train Decomposition

    00:12:04
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    With the advances in data acquisition technology, tensor objects are collected in a variety of applications including multimedia, medical and hyperspectral imaging. As the dimensionality of tensor objects is usually very high, dimensionality reduction is
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  • Learning To Separate Sounds From Weakly Labeled Scenes

    00:12:59
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    Deep learning models for monaural audio source separation are typically trained on large collections of isolated sources, which may not be available in domains such as environmental monitoring. We propose objective functions and network architectures that
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  • Exact Sparse Nonnegative Least Squares

    00:14:47
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    We propose a novel approach to solve exactly the sparse nonnegative least squares problem, under hard l0 sparsity constraints. This approach is based on a dedicated branch-and-bound algorithm. This simple strategy is able to compute the optimal solution e
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