IEEE ICASSP 2020 Virtual Conference May 2020

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  • Continuous Speech Separation: Dataset And Analysis

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    This paper describes a dataset and protocols for evaluating continuous speech separation (CSS) algorithms. Most prior studies on speech separation use pre-segmented signals of artificially mixed sufficiently overlapped utterances, and the algorithms are e
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  • Adversarial Networks For Secure Wireless Communications

    00:12:46
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    We propose a data-driven secure wireless communication scheme, in which the goal is to transmit a signal to a legitimate receiver with minimal distortion, while keeping some information about the signal private from an eavesdropping adversary. When the da
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  • Learning Product Graphs From Multidomain Signals

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    In this paper, we focus on learning the underlying product graph structure from multidomain training data. We assume that the product graph is formed from a Cartesian graph product of two smaller factor graphs. We then pose the product graph learning prob
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This paper addresses the problem of multitarget tracking using a network of mobile sensors with unknown positions. In contrast to commonly-used approaches which split the sensor localization and target tracking into two different subproblems, we propose a
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  • An Empirical Study Of Conv-Tasnet

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    Conv-TasNet is a recently proposed waveform-based deep neural network that achieves state-of-the-art performance in speech source separation. Its architecture consists of a learnable encoder/decoder and a separator that operates on top of this learned spa
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  • Speaker Augmentation For Low Resource Speech Recognition

    00:12:03
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    Text-to-speech synthesis(TTS) is often applied as a data augmentation approach for automatic speech recognition(ASR), leveraging additional texts for ASR training. However, in low resource dataset, only a limited number of speakers are available, leading
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  • An Efficient Coupled Dictionary Learning Method

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    In this letter, we present a generic and computationally efficient method for coupled dictionary learning (CDL). The proposed method enforces relations between the corresponding atoms of dictionaries learned to represent two related (but not necessarily o
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  • Nus Auto Lyrix Align

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    NUS Auto Lyrix Align is a system that automatically provides word-level alignment of a given lyrics text to a given polyphonic song. Automatic lyrics alignment in polyphonic music is a challenging task because the singing vocals are corrupted by the backg
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  • Adaptation And Learning In Multi-Task Decision Systems

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    Adaptation and learning over multi-agent networks is a topic of great relevance with important implications. Elaborating on previous works on single-task networks engaged in decision problems, here we consider the multi-task version in the challenging sce
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