IEEE ICASSP 2020 Virtual Conference May 2020

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  • Fixed-Point Optimization Of Transformer Neural Network

    00:12:51
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    The Transformer model adopts a self-attention structure and shows very good performance in various natural language processing tasks. However, it is difficult to implement the Transformer in embedded systems because of its very large model size. In this s
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  • Anisotropic Guided Filtering

    00:19:10
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    The guided filter and its derivatives have been widely employed in many image processing and computer vision applications due to their low complexity and good edge-preservation properties. Despite this success, these variants are unable to handle more agg
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  • Fast High-Dimensional Kernel Filtering

    00:12:35
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    The bilateral and nonlocal means filters are instances of kernel-based filters that are popularly used in image processing. It was recently shown that fast and accurate bilateral filtering of grayscale images can be performed using a low-rank approximatio
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  • Enriched Speech For Effortless Listening

    00:13:42
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    Human-machine speech interaction is increasingly common in the industrialised world. A (natural or synthetic) speech output that is optimised for high intelligibility and low cognitive load is of interest for both academia and industry: ENRICH (www.enrich
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  • Video-Driven Speech Reconstruction

    00:15:00
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    This demo will showcase our video-to-audio model which attempts to reconstruct speech from short videos of spoken statements. Our model does so in a completely end-to-end manner where raw audio is generated based on the input video. This approach bypasses
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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

    00:13:02
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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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