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ICASSP is the world’s largest and most comprehensive technical conference focused on signal processing and its applications. The ICASSP 2020 conference will feature world-class presentations by internationally renowned speakers, cutting-edge session topics and provide a fantastic opportunity to network with like-minded professionals from around the world. Visit website.
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- Read more about Look globally, age locally: Face aging with an attention mechanism
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Face aging is of great importance for cross-age recognition and entertainment-related applications. Recently, conditional generative adversarial networks (cGANs) have achieved impressive results for facial aging. Existing cGANs-based methods usually require a pixel-wise loss to keep the identity and background consistent. However, minimizing the pixel-wise loss between the input and synthesized images likely resulting in a ghosted or blurry face.
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- Read more about Graph Neural Net using Analytical Graph Filters and Topology Optimization for Image Denoising
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While convolutional neural nets (CNN) have achieved remarkable performance for a wide range of inverse imaging applications, the filter coefficients are computed in a purely data-driven manner and are not explainable. Inspired by an analytically derived CNN by Hadji et al., in this paper we construct a new layered graph convolutional neural net (GCNN) using GraphBio as our graph filter.
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- Read more about NORMALIZED LEAST-MEAN-SQUARE ALGORITHMS WITH MINIMAX CONCAVE PENALTY
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We propose a novel problem formulation for sparsity-aware adaptive filtering based on the nonconvex minimax concave (MC) penalty, aiming to obtain a sparse solution with small estimation bias. We present two algorithms: the first algorithm uses a single firm-shrinkage operation, while the second one uses double soft-shrinkage operations. The twin soft-shrinkage operations compensate each other, promoting sparsity while avoiding a serious increase of biases. The whole cost function is convex in certain parameter settings, while the instantaneous cost function is always nonconvex.
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- Read more about Mr Nikolajs Skuratovs
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In this paper we consider the problem of recovering a signal x of size N from noisy and compressed measurements y = A x + w of size M, where the measurement matrix A is right-orthogonally invariant (ROI). Vector Approximate Message Passing (VAMP) demonstrates great reconstruction results for even highly ill-conditioned matrices A in relatively few iterations. However, performing each iteration is challenging due to either computational or memory point of view.
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- Read more about Frame-based Overlapping Speech Detection using Convolutional Neural Networks
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Naturalistic speech recordings usually contain speech signals from multiple speakers. This phenomenon can degrade the performance of speech technologies due to the complexity of tracing and recognizing individual speakers. In this study, we investigate the detection of overlapping speech on segments as short as 25 ms using Convolutional Neural Networks. We evaluate the detection performance using different spectral features, and show that pyknogram features outperforms other commonly used speech features.
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- Read more about LOW MUTUAL AND AVERAGE COHERENCE DICTIONARY LEARNING USING CONVEX APPROXIMATION
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- Read more about Adaptive prediction of financial time-series for decision-making using a tensorial aggregation approach
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Economic and financial decision-making may cause a significant impact on government, society, and industries. Due to the increasing volume of data, decision science has become an interdisciplinary field of study, supported by efficient methods and models of data analysis. Our contributions lie exactly in the intersection of signal processing, tensorial algebra, and decision science. More precisely, we introduce a novel approach in which the data taken into account in the decision process is modeled as a tensor.
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- Read more about DEEP LEARNING FOR ROBUST POWER CONTROL FOR WIRELESS NETWORKS
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- Read more about Soft-Output Finite Alphabet Equalization for mmWave Massive MIMO
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