884 research outputs found

    Restoration of multichannel microwave radiometric images

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    A constrained iterative image restoration method is applied to multichannel diffraction-limited imagery. This method is based on the Gerchberg-Papoulis algorithm utilizing incomplete information and partial constraints. The procedure is described using the orthogonal projection operators which project onto two prescribed subspaces iteratively. Some of its properties and limitations are also presented. The selection of appropriate constraints was emphasized in a practical application. Multichannel microwave images, each having different spatial resolution, were restored to a common highest resolution to demonstrate the effectiveness of the method. Both noise-free and noisy images were used in this investigation

    Sperry Univac speech communications technology

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    Technology and systems for effective verbal communication with computers were developed. A continuous speech recognition system for verbal input, a word spotting system to locate key words in conversational speech, prosodic tools to aid speech analysis, and a prerecorded voice response system for speech output are described

    Single channel nonstationary signal separation using linear time-varying filters

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    Techniques for the Regeneration of Wideband Speech from Narrowband Speech

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    This paper addresses the problem of reconstructing wideband speech signals from observed narrowband speech signals. The goal of this work is to improve the perceived quality of speech signals which have been transmitted through narrowband channels or degraded during acquisition. We describe a system, based on linear predictive coding, for estimating wideband speech from narrowband. This system employs both previously identified and novel techniques. Experimental results are provided in order to illustrate the system’s ability to improve speech quality. Both objective and subjective criteria are used to evaluate the quality of the processed speech signals

    Adaptive phase transform processors for time delay estimation

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    Journal ArticleThis paper introduces two recursive realizations of the phase transform (PHAT) processor for time-delay estimation (TDE), using a simple one-pole low-pass filter and the least-mean-square (LMS) adaptive filter, respectively. It is shown that these adaptive methods are capable of tracking time-varying delay functions which correspond to moving sources or receivers, and are very effective in reducing the effect of interfering tonals which must be generated by the target as jamming signals to mask its movement. The performances of these methods are compared with those of other existing adaptive TDE algorithma via computer simulations

    Digital Signal Processing

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    Contains an introduction and reports on fifteen research projects.National Science Foundation FellowshipU.S. Navy - Office of Naval Research (Contract N00014-81-K-0742)National Science Foundation (Grant ECS 84-07285)Sanders Associates, Inc.U.S. Air Force - Office of Scientific Research (Contract F19628-85-K-0028)AT&T Bell Laboratories Doctoral Support ProgramCanada, Bell Northern Research ScholarshipCanada, Fonds pour la Formation de Chercheurs et /'Aide a la Recherche Postgraduate FellowshipCanada, Natural Science and Engineering Research Council Postgraduate FellowshipAmoco Foundation FellowshipFannie and John Hertz Foundation Fellowshi

    Sampling Sparse Signals on the Sphere: Algorithms and Applications

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    We propose a sampling scheme that can perfectly reconstruct a collection of spikes on the sphere from samples of their lowpass-filtered observations. Central to our algorithm is a generalization of the annihilating filter method, a tool widely used in array signal processing and finite-rate-of-innovation (FRI) sampling. The proposed algorithm can reconstruct KK spikes from (K+K)2(K+\sqrt{K})^2 spatial samples. This sampling requirement improves over previously known FRI sampling schemes on the sphere by a factor of four for large KK. We showcase the versatility of the proposed algorithm by applying it to three different problems: 1) sampling diffusion processes induced by localized sources on the sphere, 2) shot noise removal, and 3) sound source localization (SSL) by a spherical microphone array. In particular, we show how SSL can be reformulated as a spherical sparse sampling problem.Comment: 14 pages, 8 figures, submitted to IEEE Transactions on Signal Processin

    HD-DEMUCS: General Speech Restoration with Heterogeneous Decoders

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    This paper introduces an end-to-end neural speech restoration model, HD-DEMUCS, demonstrating efficacy across multiple distortion environments. Unlike conventional approaches that employ cascading frameworks to remove undesirable noise first and then restore missing signal components, our model performs these tasks in parallel using two heterogeneous decoder networks. Based on the U-Net style encoder-decoder framework, we attach an additional decoder so that each decoder network performs noise suppression or restoration separately. We carefully design each decoder architecture to operate appropriately depending on its objectives. Additionally, we improve performance by leveraging a learnable weighting factor, aggregating the two decoder output waveforms. Experimental results with objective metrics across various environments clearly demonstrate the effectiveness of our approach over a single decoder or multi-stage systems for general speech restoration task.Comment: Accepted by INTERSPEECH 202

    Graph Signal Processing: Overview, Challenges and Applications

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    Research in Graph Signal Processing (GSP) aims to develop tools for processing data defined on irregular graph domains. In this paper we first provide an overview of core ideas in GSP and their connection to conventional digital signal processing. We then summarize recent developments in developing basic GSP tools, including methods for sampling, filtering or graph learning. Next, we review progress in several application areas using GSP, including processing and analysis of sensor network data, biological data, and applications to image processing and machine learning. We finish by providing a brief historical perspective to highlight how concepts recently developed in GSP build on top of prior research in other areas.Comment: To appear, Proceedings of the IEE
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