mailitics

Tag: signal

  • Learning Time-Varying Graphs from Incomplete Graph Signals

    Learning Time-Varying Graphs from Incomplete Graph Signals arXiv:2510.17903v1 Announce Type: new Abstract: This paper tackles the challenging problem of jointly inferring time-varying network topologies and imputing missing data from partially observed graph signals. We propose a unified non-convex optimization framework to simultaneously recover a sequence of graph Laplacian matrices while reconstructing the unobserved signal entries.…

  • Field of View Enhanced Signal Dependent Binauralization with Mixture of Experts Framework for Continuous Source Motion

    Field of View Enhanced Signal Dependent Binauralization with Mixture of Experts Framework for Continuous Source Motion arXiv:2509.13548v1 Announce Type: cross Abstract: We propose a novel mixture of experts framework for field-of-view enhancement in binaural signal matching. Our approach enables dynamic spatial audio rendering that adapts to continuous talker motion, allowing users to emphasize or suppress…

  • Graph Signal Inference by Learning Narrowband Spectral Kernels

    Graph Signal Inference by Learning Narrowband Spectral Kernels arXiv:2502.13686v1 Announce Type: new Abstract: While a common assumption in graph signal analysis is the smoothness of the signals or the band-limitedness of their spectrum, in many instances the spectrum of real graph data may be concentrated at multiple regions of the spectrum, possibly including mid-to-high-frequency components.…

  • Signal Recovery Using a Spiked Mixture Model

    Signal Recovery Using a Spiked Mixture Model arXiv:2501.01840v1 Announce Type: new Abstract: We introduce the spiked mixture model (SMM) to address the problem of estimating a set of signals from many randomly scaled and noisy observations. Subsequently, we design a novel expectation-maximization (EM) algorithm to recover all parameters of the SMM. Numerical experiments show that…

  • GPS Interpolation Using Maps and Kinematics

    GPS Interpolation Using Maps and Kinematics How do you apply dead reckoning to your geospatial dataset? The picture above illustrates the GPS interpolation process. The red dots represent the known and repeated GPS locations, with more than one location per dot, while the blue dots represent the inferred locations of the repeated points along the…