IEEE ICASSP 2020 Virtual Conference May 2020

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  • Duration Robust Weakly Supervised Sound Event Detection

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    Task 4 of the DCASE2018 challenge demonstrated that substantially more research is needed for a real-world application of sound event detection. Analyzing the challenge results it can be seen that most successful models are biased towards predicting long
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  • Target Parameter Estimation Via One-Bit Pmcw Radar

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    We consider the problem of phase modulated continuous wave (PMCW) radar signal processing when the receiver utilizes one-bit sampling with known time-varying thresholds. We formulate the target parameter estimation problem as a sparse signal recovery prob
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  • Demystifying Tasnet: A Dissecting Approach

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    In recent years time domain speech separation has excelled over frequency domain separation in single channel scenarios and noise-free environments. In this paper we dissect the gains of the time-domain audio separation network (TasNet) approach by gradua
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  • Speaker Diarization With Region Proposal Network

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    Speaker diarization is an important pre-processing step for many speech applications, and it aims to solve the "who spoke when" problem. The standard diarization systems can achieve satisfactory results in various scenarios, but they are composed of sever
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  • A Dataset For Measuring Reading Levels In India At Scale

    00:10:51
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    One out of four children in India are leaving grade eight without basic reading skills. Measuring the reading levels in a vast country like India poses significant hurdles. Recent advances in machine learning opens up the possibility of automating this ta
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Recent studies have highlighted adversarial examples as ubiquitous threats to the deep neural network (DNN) based speech recognition systems. In this work, we present a U-Net based attention model, U-Net$_{At}$, to enhance adversarial speech signals. Spec
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  • Classifying Anomalies For Network Security

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    Detecting and classifying anomalous behaviors in computer networks remains a formidable challenge. This work outlines a machine learning technique that uses deep neural networks to detect and classify a variety of network attacks. Our approach is based on
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  • Pitch Estimation Via Self-Supervision

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    We present a method to estimate the fundamental frequency in monophonic audio, often referred to as pitch estimation. In contrast to existing methods, our neural network can be fully trained only on unlabeled data, using self-supervision. A tiny amount of
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Multiple Sclerosis (MS) lesion segmentation from MR images is important for neuroimaging analysis. MS is diffuse, multifocal, and tend to involve peripheral brain structures such as the white matter, corpus callosum, and brainstem. Recently, U-Net has mad
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