151,830 research outputs found

    Audio Analysis/synthesis System

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    A method and apparatus for the automatic analysis, synthesis and modification of audio signals, based on an overlap-add sinusoidal model, is disclosed. Automatic analysis of amplitude, frequency and phase parameters of the model is achieved using an analysis-by-synthesis procedure which incorporates successive approximation, yielding synthetic waveforms which are very good approximations to the original waveforms and are perceptually identical to the original sounds. A generalized overlap-add sinusoidal model is introduced which can modify audio signals without objectionable artifacts. In addition, a new approach to pitch-scale modification allows for the use of arbitrary spectral envelope estimates and addresses the problems of high-frequency loss and noise amplification encountered with prior art methods. The overlap-add synthesis method provides the ability to synthesize sounds with computational efficiency rivaling that of synthesis using the discrete short-time Fourier transform (DSTFT) while eliminating the modification artifacts associated with that method.Georgia Tech Research Corporatio

    Rethinking tropical phenology: insights from long‐term monitoring and novel analytical methods

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    Here, we introduce the Special Section (SS) on long‐term monitoring and new analytical methods in tropical phenology. The SS puts together nine original papers plus a synthesis, bringing significant advances and new insights into our understanding of tropical phenology across Africa and tropical America. The papers address environmental cues, methodological shortcomings, and provide innovative analytical approaches, opening new pathways, perspective and applications of tropical phenology for forest management and environmental monitoring. The SS is a substantial step toward a more comprehensive overview of trends in tropical phenology, as seven of nine studies evaluate >10‐yr data sets applying new methods of analysis such as hierarchical Bayesian models, generalized additive models, and Fourier analysis. We argue that it is essential to maintain ongoing monitoring programs and build a tropical phenology network at least for long‐term (>10 yr) study sites, providing the means for national and international financial support. Cross‐continental comparisons are now a primary goal, as we work toward a global vision of trends and shifts in tropical phenology in the Anthropocene

    A Fast and Accurate Algorithm for Spherical Harmonic Analysis on HEALPix Grids with Applications to the Cosmic Microwave Background Radiation

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    The Hierarchical Equal Area isoLatitude Pixelation (HEALPix) scheme is used extensively in astrophysics for data collection and analysis on the sphere. The scheme was originally designed for studying the Cosmic Microwave Background (CMB) radiation, which represents the first light to travel during the early stages of the universe's development and gives the strongest evidence for the Big Bang theory to date. Refined analysis of the CMB angular power spectrum can lead to revolutionary developments in understanding the nature of dark matter and dark energy. In this paper, we present a new method for performing spherical harmonic analysis for HEALPix data, which is a central component to computing and analyzing the angular power spectrum of the massive CMB data sets. The method uses a novel combination of a non-uniform fast Fourier transform, the double Fourier sphere method, and Slevinsky's fast spherical harmonic transform (Slevinsky, 2019). For a HEALPix grid with NN pixels (points), the computational complexity of the method is O(Nlog2N)\mathcal{O}(N\log^2 N), with an initial set-up cost of O(N3/2logN)\mathcal{O}(N^{3/2}\log N). This compares favorably with O(N3/2)\mathcal{O}(N^{3/2}) runtime complexity of the current methods available in the HEALPix software when multiple maps need to be analyzed at the same time. Using numerical experiments, we demonstrate that the new method also appears to provide better accuracy over the entire angular power spectrum of synthetic data when compared to the current methods, with a convergence rate at least two times higher

    Texture Synthesis Through Convolutional Neural Networks and Spectrum Constraints

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    This paper presents a significant improvement for the synthesis of texture images using convolutional neural networks (CNNs), making use of constraints on the Fourier spectrum of the results. More precisely, the texture synthesis is regarded as a constrained optimization problem, with constraints conditioning both the Fourier spectrum and statistical features learned by CNNs. In contrast with existing methods, the presented method inherits from previous CNN approaches the ability to depict local structures and fine scale details, and at the same time yields coherent large scale structures, even in the case of quasi-periodic images. This is done at no extra computational cost. Synthesis experiments on various images show a clear improvement compared to a recent state-of-the art method relying on CNN constraints only

    The non-coplanar baselines effect in radio interferometry: The W-Projection algorithm

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    We consider a troublesome form of non-isoplanatism in synthesis radio telescopes: non-coplanar baselines. We present a novel interpretation of the non-coplanar baselines effect as being due to differential Fresnel diffraction in the neighborhood of the array antennas. We have developed a new algorithm to deal with this effect. Our new algorithm, which we call "W-projection", has markedly superior performance compared to existing algorithms. At roughly equivalent levels of accuracy, W-projection can be up to an order of magnitude faster than the corresponding facet-based algorithms. Furthermore, the precision of result is not tightly coupled to computing time. W-projection has important consequences for the design and operation of the new generation of radio telescopes operating at centimeter and longer wavelengths.Comment: Accepted for publication in "IEEE Journal of Selected Topics in Signal Processing

    For the Jubilee of Vladimir Mikhailovich Chernov

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    On April 25, 2019, Vladimir Chernov celebrated his 70th birthday, Doctor of Physics and Mathematics, Chief Researcher at the Laboratory of Mathematical Methods of Image Processing of the Image Processing Systems Institute of the Russian Academy of Sciences (IPSI RAS), a branch of the Federal Science Research Center "Crystallography and Photonics RAS and part-Time Professor at the Department of Geoinformatics and Information Security of the Samara National Research University named after academician S.P. Korolev (Samara University). The article briefly describes the scientific and pedagogical achievements of the hero of the day. © Published under licence by IOP Publishing Ltd
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