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Noise measurement

About: Noise measurement is a research topic. Over the lifetime, 19776 publications have been published within this topic receiving 308180 citations.


Papers
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Journal ArticleDOI
TL;DR: The authors describe how a wide range of Probabilistic information pertaining to the noise process can be used in a general set theoretic estimation framework to constrain the sample statistics of the estimation residual to be consistent with those probabilistic properties of the noise which are available and to construct sets accordingly in the solution space.
Abstract: In most digital signal processing problems, the goal is to estimate an object from noise corrupted observations of a physical system. The authors describe how a wide range of probabilistic information pertaining to the noise process can be used in a general set theoretic estimation framework. The basic principle is to constrain the sample statistics of the estimation residual to be consistent with those probabilistic properties of the noise which are available and to construct sets accordingly in the solution space. Adding these sets to the collection of sets describing the solution will yield a smaller feasibility set and, hence, more reliable estimates. Pieces of information relative to quantities such as range, moments, absolute moments, and second and higher order probabilistic attributes are considered, and properties of the corresponding sets are established. Simulations are provided to illustrate the theoretical developments. >

68 citations

Proceedings ArticleDOI
05 Mar 2015
TL;DR: A brief analysis of different techniques used for speckle noise reduction, along with their advantages and disadvantages, in a comparative manner are presented.
Abstract: Noise refers to the random variation of intensity of a pixel, which modifies the actual information of the image. As a result, pixels which appear in the image are not the actual pixels. Addition of extraneous values to the image causes the occurrence of noise. Noise is categorized into impulse (salt-and-pepper) noise, uniform noise, Gaussian noise, exponential noise, Erlang (gamma) noise, photon noise, speckle noise, etc. Speckle noise is the noise that arises due to the effect of environmental conditions on the imaging sensor during image acquisition. Speckle noise is mostly detected in case of medical images, active Radar images and Synthetic Aperture Radar (SAR) images. Various researchers have performed experiments to overcome this kind of noise using different filtering techniques based on soft computing approaches, such as Fuzzy Filter, Genetic Algorithm, Particle Swarm Optimization, Artificial Bee Colony Optimization, Neural Networks, etc. In this paper, we present a brief analysis of different techniques used for speckle noise reduction, along with their advantages and disadvantages, in a comparative manner.

68 citations

Journal ArticleDOI
TL;DR: The results demonstrate that the optimal performance of the MVDR beamformer occurs when the source is in the endfire directions for diffuse noise and point-source noise while its SNR gain does not depend on the signal incidence angle in spatially white noise.
Abstract: Linear microphone arrays combined with the minimum variance distortionless response (MVDR) beamformer have been widely studied in various applications to acquire desired signals and reduce the unwanted noise. Most of the existing array systems assume that the desired sources are in the broadside direction. In this paper, we study and analyze the performance of the MVDR beamformer as a function of the source incidence angle. Using the signal-to-noise ratio (SNR) and beampattern as the criteria, we investigate its performance in four different scenarios: spatially white noise, diffuse noise, diffuse-plus-white noise, and point-source-plus-white noise. The results demonstrate that the optimal performance of the MVDR beamformer occurs when the source is in the endfire directions for diffuse noise and point-source noise while its SNR gain does not depend on the signal incidence angle in spatially white noise. This indicates that most current systems may not fully exploit the potential of the MVDR beamformer. This analysis does not only help us better understand this algorithm, but also helps us design better array systems for practical applications.

68 citations

Patent
31 Aug 2005
TL;DR: In this paper, a method that may be used in variety of electronic devices for generating comfort noise includes receiving a plurality of information frames indicative of speech plus background noise, estimating one or more background noise characteristics based on the plurality of Information frames, and generating a comfort noise signal based on one or multiple background noises characteristics.
Abstract: A method that may be used in variety of electronic devices for generating comfort noise includes receiving a plurality of information frames indicative of speech plus background noise, estimating one or more background noise characteristics based on the plurality of information frames, and generating a comfort noise signal based on the one or more background noise characteristics. The method may further include generating a speech signal from the plurality of information frames, and generating an output signal by switching between the comfort noise signal and the speech signal based on a voice activity detection.

67 citations

Journal ArticleDOI
TL;DR: A new online methodology to reduce noise in CGM signals by a Kalman filter (KF), whose unknown parameters are adjusted in a given individual by a stochastically based smoothing criterion exploiting data of a burn-in interval is presented.
Abstract: Continuous glucose monitoring (CGM) devices can be very useful in diabetes management. Unfortunately, their use in online applications, e.g., for hypo/hyperalert generation, is made difficult by random noise measurement. Remarkably, the SNR of CGM data varies with the sensor and with the individual. As a consequence, approaches in which filter parameters are not allowed to adapt to the current SNR are likely to be suboptimal. In this paper, we present a new online methodology to reduce noise in CGM signals by a Kalman filter (KF), whose unknown parameters are adjusted in a given individual by a stochastically based smoothing criterion exploiting data of a burn-in interval. The performance of the new KF approach is quantitatively assessed on Monte Carlo simulations and 24 real CGM datasets. Our results are compared with those obtained by a moving-average (MA) filtering approach with fixed parameters currently in use in likely all commercial CGM devices. Results show that the new KF approach performs much better than MA. For instance, on real data, for comparable signal denoising, the delay introduced by KF is about 35% less than that obtained by MA.

67 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202377
2022162
2021495
2020525
2019489
2018755