IEEE ICASSP 2020 Virtual Conference May 2020

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  • Low-Complexity 5G Slam With Ckf-Phd Filter

    00:12:48
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    In 5G mmWave, simultaneous localization and mapping (SLAM) allows devices to exploit map information to improve their position estimate. Even the most basic SLAM filter based on a Rao-Blackwellized particle filter (RBPF) combined with a probability hypoth
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  • Privacy-Aware Quickest Change Detection

    00:14:55
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    This paper considers the problem of the quickest detection of a change in distribution while taking privacy considerations into account. Our goal is to sanitize the signal to satisfy information privacy requirements while being able to detect a change qui
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  • Speech Breathing Estimation Using Deep Learning Methods

    00:12:00
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    Breathing is the primary mechanism for maintaining the sub-glottal pressure for speech production. Speech can be seen as a systematic outflow of air during exhalation characterized by linguistic content and prosodic factors. Thus, sensing respiratory dyna
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  • Learning To Characterize Adversarial Subspaces

    00:13:07
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    Deep Neural Networks (DNNs) are known to be vulnerable to the maliciously generated adversarial examples. To detect these adversarial examples, previous methods use artificially designed metrics to characterize the properties of adversarial subspaces wher
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  • Self-Supervised Adversarial Training

    00:12:06
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    Recent work has demonstrated that neural networks are vulnerable to adversarial examples. To escape from the predicament, many works try to harden the model in various ways, in which adversarial training is an effective way which learns robust feature rep
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  • Detecting Adversarial Attacks In Time-Series Data

    00:14:24
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    In recent times, deep neural networks have seen increased adoption in highly critical tasks. They are also susceptible to adversarial attacks, which are specifically crafted changes made to input samples which lead to erroneous output from such models. Su
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  • No-Regret Non-Convex Online Meta-Learning

    00:12:11
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    The online meta-learning framework is designed for the continual lifelong learning setting. It bridges two fields: meta-learning which tries to extract prior knowledge from past tasks for fast learning of future tasks, and online-learning which tackles th
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  • Detection And Analysis Of T/D Deletion In Librispeech

    00:14:32
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    In this study we developed a new method for automatic identification of t/d deletion. Our method achieved 94% accuracy on TIMIT and 87% on human-annotated data from Librispeech. We then conducted an analysis of t/d deletion on more than half of a million
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