Showing papers on "Noise published in 2009"

Hardware-Efficient Coherent Digital Receiver Concept With Feedforward Carrier Recovery for $M$ -QAM Constellations

Abstract: This paper presents a novel digital feedforward carrier recovery algorithm for arbitrary M-ary quadrature amplitude modulation (M-QAM) constellations in an intradyne coherent optical receiver. The approach does not contain any feedback loop and is therefore highly tolerant against laser phase noise. This is crucial, especially for higher order QAM constellations, which inherently have a smaller phase noise tolerance due to the lower spacing between adjacent constellation points. In addition to the mathematical description of the proposed carrier recovery algorithm also a possible hardware-efficient implementation in a parallelized system is presented and the performance of the algorithm is evaluated by Monte Carlo simulations for square 4-QAM (QPSK), 16-QAM, 64-QAM, and 256-QAM. For the simulations ASE noise and laser phase noise are considered as well as analog-to-digital converter (ADC) and internal resolution effects. For a 1 dB penalty at BER = 10-3, linewidth times symbol duration products of 4.1 x 10-4 (4-QAM), 1.4 x 10-4 (16-QAM), 4.0 x 10-5 (64-QAM) and 8.0 x 10-6 (256-QAM) are tolerable.

Hidden Markov map matching through noise and sparseness

Abstract: The problem of matching measured latitude/longitude points to roads is becoming increasingly important This paper describes a novel, principled map matching algorithm that uses a Hidden Markov Model (HMM) to find the most likely road route represented by a time-stamped sequence of latitude/longitude pairs The HMM elegantly accounts for measurement noise and the layout of the road network We test our algorithm on ground truth data collected from a GPS receiver in a vehicle Our test shows how the algorithm breaks down as the sampling rate of the GPS is reduced We also test the effect of increasing amounts of additional measurement noise in order to assess how well our algorithm could deal with the inaccuracies of other location measurement systems, such as those based on WiFi and cell tower multilateration We provide our GPS data and road network representation as a standard test set for other researchers to use in their map matching work

Iterative Weighted Maximum Likelihood Denoising With Probabilistic Patch-Based Weights

Abstract: Image denoising is an important problem in image processing since noise may interfere with visual or automatic interpretation. This paper presents a new approach for image denoising in the case of a known uncorrelated noise model. The proposed filter is an extension of the nonlocal means (NL means) algorithm introduced by Buades, which performs a weighted average of the values of similar pixels. Pixel similarity is defined in NL means as the Euclidean distance between patches (rectangular windows centered on each two pixels). In this paper, a more general and statistically grounded similarity criterion is proposed which depends on the noise distribution model. The denoising process is expressed as a weighted maximum likelihood estimation problem where the weights are derived in a data-driven way. These weights can be iteratively refined based on both the similarity between noisy patches and the similarity of patches extracted from the previous estimate. We show that this iterative process noticeably improves the denoising performance, especially in the case of low signal-to-noise ratio images such as synthetic aperture radar (SAR) images. Numerical experiments illustrate that the technique can be successfully applied to the classical case of additive Gaussian noise but also to cases such as multiplicative speckle noise. The proposed denoising technique seems to improve on the state of the art performance in that latter case.

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Abstract: Image denoising is an important problem in image processing since noise may interfere with visual or automatic interpretation. This paper presents a new approach for image denoising in the case of a known uncorrelated noise model. The proposed filter is an extension of the Non Local means (NL means) algorithm introduced by Buades et al. (1), which performs a weighted average of the values of similar pixels. Pixel similarity is defined in NL means as the Euclidean distance between patches (rectangular windows centered on each two pixels). In this paper a more general and statistically grounded similarity criterion is proposed which depends on the noise distribution model. The denoising process is expressed as a weighted maximum likelihood estimation problem where the weights are derived in a data- driven way. These weights can be iteratively refined based on both the similarity between noisy patches and the similarity of patches extracted from the previous estimate. We show that this iterative process noticeably improves the denoising performance, especially in the case of low signal-to-noise ratio images such as Synthetic Aperture Radar (SAR) images. Numerical experiments illustrate that the technique can be successfully applied to the classical case of additive Gaussian noise but also to cases such as multiplicative speckle noise. The proposed denoising technique seems to improve on the state of the art performance in that latter case.

Recursive Noise Adaptive Kalman Filtering by Variational Bayesian Approximations

Abstract: This article considers the application of variational Bayesian methods to joint recursive estimation of the dynamic state and the time-varying measurement noise parameters in linear state space models. The proposed adaptive Kalman filtering method is based on forming a separable variational approximation to the joint posterior distribution of states and noise parameters on each time step separately. The result is a recursive algorithm, where on each step the state is estimated with Kalman filter and the sufficient statistics of the noise variances are estimated with a fixed-point iteration. The performance of the algorithm is demonstrated with simulated data.

Financial time series forecasting using independent component analysis and support vector regression

Abstract: As financial time series are inherently noisy and non-stationary, it is regarded as one of the most challenging applications of time series forecasting. Due to the advantages of generalization capability in obtaining a unique solution, support vector regression (SVR) has also been successfully applied in financial time series forecasting. In the modeling of financial time series using SVR, one of the key problems is the inherent high noise. Thus, detecting and removing the noise are important but difficult tasks when building an SVR forecasting model. To alleviate the influence of noise, a two-stage modeling approach using independent component analysis (ICA) and support vector regression is proposed in financial time series forecasting. ICA is a novel statistical signal processing technique that was originally proposed to find the latent source signals from observed mixture signals without having any prior knowledge of the mixing mechanism. The proposed approach first uses ICA to the forecasting variables for generating the independent components (ICs). After identifying and removing the ICs containing the noise, the rest of the ICs are then used to reconstruct the forecasting variables which contain less noise and served as the input variables of the SVR forecasting model. In order to evaluate the performance of the proposed approach, the Nikkei 225 opening index and TAIEX closing index are used as illustrative examples. Experimental results show that the proposed model outperforms the SVR model with non-filtered forecasting variables and a random walk model.

Non-genetic heterogeneity of cells in development: more than just noise.

Abstract: Cell-to-cell variability of gene expression in clonal populations of mammalian cells is ubiquitous. However, because molecular biologists habitually assume uniformity of the cell populations that serve as starting material for experimental analysis, attention to such non-genetic heterogeneity has been scant. As awareness of, and interest in, understanding its biological significance increases, this Primer attempts to clarify the confusing terminologies used in an emerging field that often conflates heterogeneity with noise, and provides a qualitative introduction to the fundamental dynamic principles that underlie heterogeneity. It thus aims to present a useful conceptual framework to organize, analyze and communicate observations made at the resolution of individual cells that indicate that heterogeneity of cell populations plays a biological role, such as in multipotency and cell fate decision.

Acoustic masking in marine ecosystems: intuitions, analysis, and implication

Abstract: Acoustic masking from anthropogenic noise is increasingly being considered as a threat to marine mammals, particularly low-frequency specialists such as baleen whales. Low-frequency ocean noise has increased in recent decades, often in habitats with seasonally resident populations of marine mammals, raising concerns that noise chronically influences life histories of individuals and populations. In contrast to physical harm from intense anthropogenic sources, which can have acute impacts on individuals, masking from chronic noise sources has been difficult to quantify at individ- ual or population levels, and resulting effects have been even more difficult to assess. This paper pre- sents an analytical paradigm to quantify changes in an animal's acoustic communication space as a result of spatial, spectral, and temporal changes in background noise, providing a functional defini- tion of communication masking for free-ranging animals and a metric to quantify the potential for communication masking. We use the sonar equation, a combination of modeling and analytical tech- niques, and measurements from empirical data to calculate time-varying spatial maps of potential communication space for singing fin (Balaenoptera physalus), singing humpback (Megoptera novaeangliae), and calling right (Eubalaena glacialis) whales. These illustrate how the measured loss of communication space as a result of differing levels of noise is converted into a time-varying mea- sure of communication masking. The proposed paradigm and mechanisms for measuring levels of communication masking can be applied to different species, contexts, acoustic habitats and ocean noise scenes to estimate the potential impacts of masking at the individual and population levels.

NoiseTube: Measuring and mapping noise pollution with mobile phones

Abstract: In this paper we present a new approach for the assessment of noise pollution involving the general public. The goal of this project is to turn GPS-equipped mobile phones into noise sensors that enable citizens to measure their personal exposure to noise in their everyday environment. Thus each user can contribute by sharing their geo-localised measurements and further personal annotation to produce a collective noise map.

Master handbook of acoustics

Abstract: Ch 1. Fundamentals of Sound Ch 2. Sound Levels and the Decibel Ch 3. The Ear and the Perception of Sound Ch 4. Speech, Music, and Noise Ch 5. Reverberation Ch 6. Absorption of Sound Ch 7. Reflection of Sound Ch 8. Diffraction of Sound Ch 9. Refraction of Sound Ch 10. Diffusion of Sound Ch 11. The Schroeder Diffuser Ch 12. Modal Resonances in Enclosed Spaces Ch 13. Sound Reflections in Enclosed Spaces Ch 14. Adjustable Acoustics Ch. 14. Control of HVAC Noise Ch. 15. Control of Interfering Noise Ch. 16. Recording Studio Acoustics Ch. 17. Studio Control Room Acoustics Ch. 18. Audio/Video Tech Room and Voice-Over Ch. 19 Home Listening Room Acoustics Ch. 20 Concert Hall Acoustics Ch. 21 Acoustical Distortion Ch. 22 Room Acoustics Measurement Software Ch. 23. Room Optimizer Ch. 24. Desktop Auralization Ch. 25. Electro-Acoustic Software for Engineers

Response to noise from modern wind farms in The Netherlands

Abstract: The increasing number and size of wind farms call for more data on human response to wind turbine noise, so that a generalized dose-response relationship can be modeled and possible adverse health effects avoided. This paper reports the results of a 2007 field study in The Netherlands with 725 respondents. A dose-response relationship between calculated A-weighted sound pressure levels and reported perception and annoyance was found. Wind turbine noise was more annoying than transportation noise or industrial noise at comparable levels, possibly due to specific sound properties such as a "swishing" quality, temporal variability, and lack of nighttime abatement. High turbine visibility enhances negative response, and having wind turbines visible from the dwelling significantly increased the risk of annoyance. Annoyance was strongly correlated with a negative attitude toward the visual impact of wind turbines on the landscape. The study further demonstrates that people who benefit economically from wind turbines have a significantly decreased risk of annoyance, despite exposure to similar sound levels. Response to wind turbine noise was similar to that found in Sweden so the dose-response relationship should be generalizable.

A New Total Variation Method for Multiplicative Noise Removal

Abstract: Multiplicative noise removal problems have attracted much attention in recent years. Unlike additive noise removal problems, the noise is multiplied to the orginal image, so almost all information of the original image may disappear in the observed image. The main aim of this paper is to propose and study a strictly convex objective function for multiplicative noise removal problems. We also incorporate the modified total variation regularization in the objective function to recover image edges. We develop an alternating minimization algorithm to find the minimizer of such an objective function efficiently and also show the convergence of the minimizing method. Our experimental results show that the quality of images denoised by the proposed method is quite good.

Testing for Shifts in Trend with an Integrated or Stationary Noise Component

Abstract: We consider testing for structural changes in the trend function of a time series without any prior knowledge of whether the noise component is stationary or integrated. Following Perron and Yabu (2009), we consider a quasi-feasible generalized least squares procedure that uses a super-efficient estimate of the sum of the autoregressive parameters αwhen α=1. This allows tests of basically the same size with stationary or integrated noise regardless of whether the break is known or unknown, provided that the Exp functional of Andrews and Ploberger (1994) is used in the latter case. To improve the finite-sample properties, we use the bias-corrected version of the estimate of αproposed by Roy and Fuller (2001). Our procedure has a power function close to that attainable if we knew the true value of αin many cases. We also discuss the extension to the case of multiple breaks.

Clinical review: The impact of noise on patients' sleep and the effectiveness of noise reduction strategies in intensive care units.

Abstract: Excessive noise is becoming a significant problem for intensive care units (ICUs). This paper first reviews the impact of noise on patients' sleep in ICUs. Five previous studies have demonstrated such impacts, whereas six other studies have shown other factors to be more important. Staff conversation and alarms are generally regarded as the most disturbing noises for patients' sleep in ICUs. Most research in this area has focused purely on noise level, but work has been very limited on the relationships between sleep quality and other acoustic parameters, including spectrum and reverberation time. Sound-absorbing treatment is a relatively effective noise reduction strategy, whereas sound masking appears to be the most effective technique for improving sleep. For future research, there should be close collaboration between medical researchers and acousticians.

Scale invariance and noise in natural images

Abstract: Natural images are known to have scale invariant statistics. While some eariler studies have reported the kurtosis of marginal bandpass filter response distributions to be constant throughout scales, other studies have reported that the kurtosis values are lower for high frequency filters than for lower frequency ones. In this work we propose a resolution for this discrepancy and suggest that this change in kurtosis values is due to noise present in the image. We suggest that this effect is consistent with a clean, natural image corrupted by white noise. We propose a model for this effect, and use it to estimate noise standard deviation in corrupted natural images. In particular, our results suggest that classical benchmark images used in low-level vision are actually noisy and can be cleaned up. Our results on noise estimation on two sets of 50 and a 100 natural images are significantly better than the state-of-the-art.

Reflection images from ambient seismic noise

Abstract: One application of seismic interferometry is to retrieve the impulse response (Green's function) from crosscorrelation of ambient seismic noise. Various researchers show results for retrieving the surface-wave part of the Green's function. However, reflection retrieval has proven more challenging. We crosscorrelate ambient seismic noise, recorded along eight parallel lines in the Sirte basin east of Ajdabeya, Libya, to obtain shot gathers that contain reflections. We take advantage of geophone groups to suppress part of the undesired surface-wave noise and apply frequency-wavenumber filtering before crosscorrelation to suppress surface waves further. After comparing the retrieved results with data from an active seismic exploration survey along the same lines, we use the retrieved reflection data to obtain a migrated reflection image of the subsurface.

Does median filtering truly preserve edges better than linear filtering

Abstract: Image processing researchers commonly assert that "median filtering is better than linear filtering for removing noise in the presence of edges." Using a straightforward large-n decision-theory framework, this folk-theorem is seen to be false in general. We show that median filtering and linear filtering have similar asymptotic worst-case mean-squared error (MSE) when the signal-to-noise ratio (SNR) is of order 1, which corresponds to the case of constant per-pixel noise level in a digital signal. To see dramatic benefits of median smoothing in an asymptotic setting, the per-pixel noise level should tend to zero (i.e., SNR should grow very large). We show that a two-stage median filtering using two very different window widths can dramatically outperform traditional linear and median filtering in settings where the underlying object has edges. In this two-stage procedure, the first pass, at a fine scale, aims at increasing the SNR. The second pass, at a coarser scale, correctly exploits the nonlinearity of the median. Image processing methods based on nonlinear partial differential equations (PDEs) are often said to improve on linear filtering in the presence of edges. Such methods seem difficult to analyze rigorously in a decision-theoretic framework. A popular example is mean curvature motion (MCM), which is formally a kind of iterated median filtering. Our results on iterated median filtering suggest that some PDE-based methods are candidates to rigorously outperform linear filtering in an asymptotic framework.

Frogs Call at a Higher Pitch in Traffic Noise

Abstract: Male frogs call to attract females for mating and to defend territories from rival males. Female frogs of some species prefer lower-pitched calls, which indicate larger, more experienced males. Acoustic interference occurs when background noise reduces the active distance or the distance over which an acoustic signal can be detected. Birds are known to call at a higher pitch or frequency in urban noise, decreasing acoustic interference from low-frequency noise. Using Bayesian linear regression, we investigated the effect of traffic noise on the pitch of advertisement calls in two species of frogs, the southern brown tree frog (Litoria ewingii) and the common eastern froglet (Crinia signifera). We found evidence that L. ewingii calls at a higher pitch in traffic noise, with an average increase in dominant frequency of 4.1 Hz/dB of traffic noise, and a total effect size of 123 Hz. This frequency shift is smaller than that observed in birds, but is still large enough to be detected by conspecific frogs and confer a significant benefit to the caller. Mathematical modelling predicted a 24% increase in the active distance of a L. ewingii call in traffic noise with a frequency shift of this size. Crinia signifera may also call at a higher pitch in traffic noise, but more data are required to be confident of this effect. Because frog calls are innate rather than learned, the frequency shift demonstrated by L. ewingii may represent an evolutionary adaptation to noisy conditions. The phenomenon of frogs calling at a higher pitch in traffic noise could therefore constitute an intriguing trade-off between audibility and attractiveness to potential mates. © 2009 by the author(s).

Vessel noise effects on delphinid communication

Abstract: Increasing numbers and speeds of vessels in areas with populations of cetaceans may have the cumulative effect of reducing habitat quality by increasing the underwater noise level. Here, we first use digital acoustic tags to demonstrate that free-ranging delphinids in a coastal deep- water habitat are subjected to varying and occasionally intense levels of vessel noise. Vessel noise and sound propagation measurements from a shallow-water habitat are then used to model the potential impact of high sound levels from small vessels on delphinid communication in both shallow and deep habitats, with bottlenose dolphins Tursiops sp. and short-finned pilot whales Globicephala macrorhynchus as model organisms. We find that small vessels travelling at 5 knots in shallow water can reduce the communication range of bottlenose dolphins within 50 m by 26%. Pilot whales in a quieter deep-water habitat could suffer a reduction in their communication range of 58% caused by a vessel at similar range and speed. Increased cavitation noise at higher speeds drastically increases the impact on the communication range. Gear shifts generate high-level transient sounds (peak- peak source levels of up to 200 dB re 1 µPa) that may be audible over many kilometres and may dis- turb close-range animals. We conclude that noise from small vessels can significantly mask acousti- cally mediated communication in delphinids and contribute to the documented negative impacts on animal fitness.

Speech Intelligibility in Background Noise with Ideal Binary Time-frequency Masking

Abstract: Ideal binary time-frequency masking is a signal separation technique that retains mixture energy in time-frequency units where local signal-to-noise ratio exceeds a certain threshold and rejects mixture energy in other time-frequency units. Two experiments were designed to assess the effects of ideal binary masking on speech intelligibility of both normal-hearing (NH) and hearing-impaired (HI) listeners in different kinds of background interference. The results from Experiment 1 demonstrate that ideal binary masking leads to substantial reductions in speech-reception threshold for both NH and HI listeners, and the reduction is greater in a cafeteria background than in a speech-shaped noise. Furthermore, listeners with hearing loss benefit more than listeners with normal hearing, particularly for cafeteria noise, and ideal masking nearly equalizes the speech intelligibility performances of NH and HI listeners in noisy backgrounds. The results from Experiment 2 suggest that ideal binary masking in the low-frequency range yields larger intelligibility improvements than in the high-frequency range, especially for listeners with hearing loss. The findings from the two experiments have major implications for understanding speech perception in noise, computational auditory scene analysis, speech enhancement, and hearing aid design.

I Like It... I Like It Not: Evaluating User Ratings Noise in Recommender Systems

Abstract: Recent growing interest in predicting and influencing consumer behavior has generated a parallel increase in research efforts on Recommender Systems. Many of the state-of-the-art Recommender Systems algorithms rely on obtaining user ratings in order to later predict unknown ratings. An underlying assumption in this approach is that the user ratings can be treated as ground truth of the user's taste. However, users are inconsistent in giving their feedback, thus introducing an unknown amount of noise that challenges the validity of this assumption. In this paper, we tackle the problem of analyzing and characterizing the noise in user feedback through ratings of movies. We present a user study aimed at quantifying the noise in user ratings that is due to inconsistencies. We measure RMSE values that range from 0.557 to 0.8156. We also analyze how factors such as item sorting and time of rating affect this noise.

Random and coherent noise attenuation by empirical mode decomposition

Abstract: We have devised a newfiltering technique for random and coherentnoiseattenuationinseismicdatabyapplyingempiricalmodedecompositionEMDonconstant-frequencyslicesinthefrequency-offsetf-xdomainandremovingthefirst intrinsicmodefunction.Themotivationbehindthisdevelopmentistoovercomethepotentiallowperformanceof f-x deconvolution for signal-to-noise enhancement when processinghighlycomplexgeologicsections,dataacquiredusingirregular trace spacing, and/or data contaminated with steeply dipping coherent noise. The resulting f-x EMD method is equivalent to an autoadaptive f-k filter with a frequency-dependent, high-wavenumber cut filtering property. Removing both random and steeply dipping coherent noise in either prestackorstacked/migratedsectionsisusefulandcompares well with other noise-reduction methods, such as f-x deconvolution,medianfiltering,andlocalsingularvaluedecomposition.Initssimplestimplementation, f-xEMDisparameterfree and can be applied to entire data sets without user interaction.

Research, part of a Special Feature on Effects of Roads and Traffic on Wildlife Populations and Landscape Function Frogs Call at a Higher Pitch in Traffic Noise

Abstract: Male frogs call to attract females for mating and to defend territories from rival males. Female frogs of some species prefer lower-pitched calls, which indicate larger, more experienced males. Acoustic interference occurs when background noise reduces the active distance or the distance over which an acoustic signal can be detected. Birds are known to call at a higher pitch or frequency in urban noise, decreasing acoustic interference from low-frequency noise. Using Bayesian linear regression, we investigated the effect of traffic noise on the pitch of advertisement calls in two species of frogs, the southern brown tree frog (Litoria ewingii) and the common eastern froglet (Crinia signifera). We found evidence that L. ewingii calls at a higher pitch in traffic noise, with an average increase in dominant frequency of 4.1 Hz/dB of traffic noise, and a total effect size of 123 Hz. This frequency shift is smaller than that observed in birds, but is still large enough to be detected by conspecific frogs and confer a significant benefit to the caller. Mathematical modelling predicted a 24% increase in the active distance of a L. ewingii call in traffic noise with a frequency shift of this size. Crinia signifera may also call at a higher pitch in traffic noise, but more data are required to be confident of this effect. Because frog calls are innate rather than learned, the frequency shift demonstrated by L. ewingii may represent an evolutionary adaptation to noisy conditions. The phenomenon of frogs calling at a higher pitch in traffic noise could therefore constitute an intriguing trade-off between audibility and attractiveness to potential mates.

Variability in motor learning: relocating, channeling and reducing noise

Abstract: Variability in motor performance decreases with practice but is never entirely eliminated, due in part to inherent motor noise. The present study develops a method that quantifies how performers can shape their performance to minimize the effects of motor noise on the result of the movement. Adopting a statistical approach on sets of data, the method quantifies three components of variability (tolerance, noise, and covariation) as costs with respect to optimal performance. T-Cost quantifies how much the result could be improved if the location of the data were optimal, N-Cost compares actual results to results with optimal dispersion at the same location, and C-Cost represents how much improvement stands to be gained if the data covaried optimally. The TNC-Cost analysis is applied to examine the learning of a throwing task that participants practiced for 6 or 15 days. Using a virtual set-up, 15 participants threw a pendular projectile in a simulated concentric force field to hit a target. Two variables, angle and velocity at release, fully determined the projectile’s trajectory and thereby the accuracy of the throw. The task is redundant and the successful solutions define a nonlinear manifold. Analysis of experimental results indicated that all three components were present and that all three decreased across practice. Changes in T-Cost were considerable at the beginning of practice; C-Cost and N-Cost diminished more slowly, with N-Cost remaining the highest. These results showed that performance variability can be reduced by three routes: by tuning tolerance, covariation and noise in execution. We speculate that by exploiting T-Cost and C-Cost, participants minimize the effects of inevitable intrinsic noise.

How Noise Trading Affects Markets: An Experimental Analysis

Abstract: We use a laboratory market to investigate the behavior of traders who lack informational advantages and have no exogenous reason to trade. We find that these uninformed traders behave largely as irrational contrarian "noise traders," trading against recent price movements to their own detriment. The uninformed traders provide some benefits to the market: increasing market volume and depth, while reducing bid-ask spreads and the temporary price impact of trades. However, their noise trading also diminishes the ability of market prices to adjust to new information. A securities transaction tax reduces uninformed trader activity, but it reduces informed trader activity by approximately the same amount; as a result, the tax does not alter the impact of noise trading on the informational efficiency of the market. The Author 2009. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org., Oxford University Press.