IEEE ICASSP 2020 Virtual Conference May 2020

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  • A Random Gossip Bmuf Process For Neural Language Modeling

    00:14:10
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    Neural network language model (NNLM) is an essential component of industrial ASR systems. One important challenge of training an NNLM is to leverage between scaling the learning process and handling big data. Conventional approaches such as block momentum
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  • Lie Group State Estimation Via Optimal Transport

    00:12:52
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    Many applications in science and engineering involve tracking the state of a stochastic differential equation (SDE) evolving in a Lie group. This has been tackled by particle filtering although some existing schemes fail to satisfy geometric constraints.
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  • Distributed Quantization For Sparse Time Sequences

    00:15:27
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    Analog signals processed in digital hardware are quantized into a discrete bit-constrained representation. Quantization is typically carried out using analog-to-digital converters (ADCs), operating in a serial scalar manner. In some applications, a set of
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Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) is a perceptual-driven approach for single image super-resolution that is able to produce photorealistic images. Despite the visual quality of these generated images, there is still room fo
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  • Vamp With Vector-Valued Diagonalization

    00:14:48
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    Vector approximate message passing is studied where vector-valued diagonalization instead of a uniform one is employed. Thereby, individual variances are tracked within the algorithm instead of an average one. Straightforward application based on the expe
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Despite the growing interest in unsupervised learning, extracting meaningful knowledge from unlabelled audio remains an open challenge. To take a step in this direction, we recently proposed a problem-agnostic speech encoder (PASE), that combines a convol
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  • Feature Affine Projection Algorithms

    00:13:56
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    There is a growing research interest in proposing new techniques to detect and exploit signals/systems sparsity. Recently, the idea of hidden sparsity has been proposed, and it has been shown that, in many cases, sparsity is not explicit, and some tools a
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  • Few-Shot Acoustic Event Detection Via Meta Learning

    00:11:59
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    We study few-shot acoustic event detection (AED) in this paper. Few-shot learning enables detection of new events with very limited labeled data and facilitates personalization of AED systems for users in real applications. Compared to other research area
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  • Distilling Attention Weights For Ctc-Based Asr Systems

    00:13:20
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    We present a novel training approach for connectionist temporal classification (CTC) -based automatic speech recognition (ASR) systems. CTC models are promising for building both a conventional acoustic model and an end-to-end (E2E) ASR model. However, CT
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  • 3D Deformation Signature For Dynamic Face Recognition

    00:15:34
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    This work proposes a novel 3D Deformation Signature (3DS) to represent a 3D deformation signal for 3D Dynamic Face Recognition. 3DS is computed given a non-linear 6D-space representation which guarantees physically plausible 3D deformations. A unique defo
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