Nonimpulsive Noise Research Articles

A simple approach for marine mammal noise-induced threshold shift prediction from down-the-hole piling noise exposure

In assessing auditory effects on marine mammals from noise exposure, a dual-criterion is currently used by regulators to estimate the noise-induced threshold shift (NITS). Such criterion classifies anthropogenic noise into two mutually exclusive categories: Impulsive and non-impulsive. However, in real world situations, marine mammals are often exposed to complex noise field that contains both impulsive and non-impulsive components, thus makes it difficult or even impossible to accurately assess their NITS from noise exposure. One example is down-the-hole (DTH) pile installation, which generates both impulsive noise from percussive drilling/striking and non-impulsive noise from debris removal simultaneously. In this study, a relatively simple approach is proposed that employs kurtosis values to quantify the impulsiveness of two DTH piling noises datasets for six different marine mammal functional hearing groups. A kurtosis adjustment approach that has been suggested for predict human hearing loss from noise exposure was then used to build correction factors for estimation of NITS of marine mammals exposed to DTH piling noises. Further research on marine mammal NITS from complex noise exposure is needed to validate and improve this model.

Robust adaptive algorithms for fast principal component analysis

In this paper, we investigate the robust estimation of the principal eigenvectors of multivariate observations in an adaptive scheme. Indeed, this problem known as PCA (Principal Component Analysis) has already many efficient solutions in batch or non-impulsive noise environment. Our objective is to develop fast adaptive methods for the PCA when the observed data is corrupted by impulsive noise or outliers or in the case of missing data. Hence, different low complexity algorithms are introduced for the estimation and tracking of the desired principal components in the three previous scenarios. Their effectiveness is illustrated via simulation experiments and comparative study.

On robust spectrum sensing using M-estimators of covariance matrix

In this paper, we consider the spectrum sensing in cognitive radio networks when the impulsive noise appears. We propose a class of blind and robust detectors using M-estimators in eigenvalue based spectrum sensing method. The conventional eigenvalue based method uses statistics derived from the eigenvalues of sample covariance matrix(SCM) as testing statistics, which are inefficient and unstable in the impulsive noise environment. Instead of SCM, we can use M-estimators, which have good performance under both impulsive and non-impulsive noise. Among those M-estimators, We recommend the Tyler's M-estimator instead, which requires no knowledge of noise distribution and have the same probability of false alarm under different complex elliptically symmetric distributions. In addition, it performs better than the detector using sample covariance matrix when the noise is highly impulsive. It should be emphasized that this detector does not require knowledge of noise power which is required by the energy detection based methods. Simulations show that it performs better than conventional detector using sample covariance matrix in a highly impulsive noise environment.

Acoustic trauma from continuous noise: Minimum exposures, issues in clinical trial design, and comments on magnetic resonance imaging exposures.

Acoustic trauma (AT) is permanent hearing loss after a single noise exposure. A few human cases resulting from continuous, i.e., nonimpulsive noise, have been reported as reviewed by Ward [(1991). "Hearing loss from noise and music," presented at Audio Engineering Society, New York, October 4-8]. This paper updates that review by examining 11 cases in nine reports, from 1950 to 2006, with the intention of determining minimum exposures that may cause AT, including the potential risk of exposure to noise from magnetic resonance imaging machines. Diffuse-field related levels above 120 dBA for 10 s or more, or above 130 dBA for 2-3 s (values well above OSHA's unprotected exposure limits), can lead to AT. These cases appear to represent a susceptible fraction of the population, because much more intense exposures (e.g., 130 dBA for 32 min) have been tolerated by groups of volunteers who suffered only temporary threshold shifts. AT from continuous noise is unlikely to occur in OSHA-compliant hearing conservation programs, and probably rare enough in the general civilian population that clinical trials of drugs aimed at treating it are unlikely to be practical. AT from impulse noise, such as gunfire, which is specifically not the topic of the current work, is more amenable to clinical trials, especially in military settings.

Noise exposure limit for children in recreational settings: Review of available evidence.

It is universally recognized that prolonged exposure to high levels of non-impulsive noise will lead to noise-induced hearing loss. These high levels of noise have traditionally been found in an occupational setting, but exposure to high levels of noise is increasingly common in recreational settings. There is currently no established acceptable risk of hearing loss in children. This review assumed that the most appropriate exposure limit for recreational noise exposure in children would be developed to protect 99% of children from hearing loss exceeding 5 dB at the 4 kHz audiometric test frequency after 18 years of noise exposure. Using the ISO 1999:2013 model for predicting hearing loss, it was estimated that noise exposure equivalent to an 8-h average exposure (LEX) of 82 dBA would result in about 4.2 dB or less of hearing loss in 99% of children after 18 years of exposure. The 8-h LEX was reduced to 80 dB to include a 2 dB margin of safety. This 8-h LEX of 80 dBA is estimated to result in 2.1 dB or less of hearing loss in 99% of children after 18 years of exposure. This is equivalent to 75 dBA as a 24-h equivalent continuous average sound level.

A Novel Adaptive State Detector-Based Post-Filtering Active Control Algorithm for Gaussian Noise Environment with Impulsive Interference

For Gaussian noise with random or periodic impulsive interference, the conventional active noise control (ANC) methods with finite second-order moments may fail to converge. Furthermore, the intensity of impulsive noise typically varies over time in the actual application, which also decreases the performance of conventional active impulsive noise control methods. To address these problems, a novel adaptive state detector based post-filtering active control algorithm is proposed. In this work, information entropy with adaptive kernel size is first introduced into the cost function of a post-filtering algorithm to improve its tracking. To enhance the robust performance of adaptive filters when impulsive interference happens, a recursive optimal threshold selecting method is also developed and analyzed by statistical theories. Simulations show that the new method has fast tracking ability in non-impulsive noise environment and keeps robust when impulsive interference happens. It also works well for the impulsive noise of different degrees. Experiment results confirm the effectiveness of the proposed algorithm.

Robust incremental normalized least mean square algorithm with variable step sizes over distributed networks

An improved incremental normalized least mean square (INLMS) algorithm is developed by minimizing the Huber cost function, which is robust against impulsive noises, over distributed networks. To significantly suppress impulsive noises, a recursive scheme based on the incremental cooperation strategy is designed for updating the cutoff parameter in the Huber function. Since the proposed algorithm can be interpreted as a variable step size INLMS algorithm, it has faster convergence rate and lower steady-state error than some existing incremental distributed algorithms in both impulsive and non-impulsive noise environments. In addition, to track a sudden change of the unknown system, a modified method of resetting the cutoff parameter is developed.

Performance Study of Novel Median Filter for Image De-Noising

Most common method of clearing image noise is through median filtration. Median smoothers discard outliers (or impulses) quite effectively at lower noise densities but, fail to provide adequate smoothing for heavily noised images for the simple reason that, in the conventional median filter approaches, each and every pixel of an image is filtered without concern about healthy pixels. To suppress this deficiency, in this paper, an improved median filter by the name Novel Median Filter (NMF) is proposed to enhance the effectiveness of the median filter at higher noise situations to provide a detail preserving de-noising of digital images. Proposed NMF algorithm is a 3-stage filter; first two stages are dedicated to selective switching median filtration in which noisy pixels are first detected and then the filtering operation is applied to restore their values with a valid (or a noise free) median computed for each 3×3 window position. Third level processing enables to remove any distortions present in the de-noised image. The proposed method, Novel Median Filter (NMF) is evaluated for its effectiveness in detail preserving de-noising capability on a large number of digital grey and color images affected with impulsive and non-impulsive noise in terms of objective and subjective quality measures. To better appraise the efficacy of the proposed method, Novel Median Filter (NMF), MATLAB simulation results in terms of peak signal to noise ratio (PSNR), mean square error (MSE) and the de-noised images are obtained and are compared with those obtained for a standard median filter (SMF) and its few variants and meaningful conclusions are derived. Detail preservation capability of the proposed filter has been demonstrated through experimentation on medical samples. General Terms Image Processing, Nonlinear Filter, Median Filtration.

Diagnosis of Array Failure in Impulsive Noise Environment Using Unsupervised Support Vector Regression Method

Fast and accurate diagnosis of array failure is important for the maintenance of array antennas. Locations of failing elements are usually detected using near field data, which may be polluted by noises. Most existing diagnosis methods assume non-impulsive noise in the measurement data. However, the practical measurement environment may contain impulsive noises, which has not been considered in existing methods. This work proposes to impose a penalty function in the residual between the measured and recovered near field. The impulsive noise in the near field data can then be suppressed by using an appropriate function as the penalty function. Furthermore, minimum ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> -norm is imposed on the excitation vector. The condition imposed on the noise and the minimum ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> -norm constraint on the excitation vector constitutes an optimization problem, which is solved using the unsupervised support vector regression. The proposed method is more accurate than existing methods when impulsive noise presents in the near field data, and it is able to deal with a wide range of number of failing elements by adjusting the value of p. Numerical results are presented to show the advantages of the proposed method and to study the choice of p.

Noise adaptive rational filter

A new rational filter (RF), called the noise adaptive rational filter (NARF), is developed for removing non-impulsive noise, such as Gaussian noise concerned in this paper. The optimal NARF, in the least mean-square (LMS) error sense, is first derived for the one-dimensional signal, followed by its extension to the two-dimensional signal through a separable approach for image denoising. Due to the feasibility issue encountered in the derived optimal closed form, a feasible (thus, sub-optimal) NARF is then proposed, which exploits noise variance (or power) to adapt to signal transitions. The vector NARF is also proposed for color image denoising. Extensive simulation results show that our proposed NARF outperforms the RF on both objective evaluation in peak signal-to-noise ratio (PSNR) measurement and subjective evaluation through visual quality assessment. Furthermore, how to exploit the proposed NARF for denoising a noisy image corrupted by a mixture of impulse noise and non-impulsive noise using a two-stage cascading approach is also discussed with some simulation results.

Development of artificial neural network classifier to identify military impulse noise

Civilian noise complaints from urban encroachment have curtailed military exercises that produce high levels of impulse noise. Currently, monitoring stations are in place around many military installations to monitor noise levels. However, these monitoring stations do not provide real-time data and suffer from false positive detections. This project sought to develop algorithms with improved noise classification accuracy. Various types of military impulse noise and nonimpulse noise were measured and processed. Approximately 1000 waveforms were field collected (670 nonimpulse and 330 military impulse). Although several metrics were examined, the conventional time domain metrics of kurtosis and crest factor as well as two novel frequency domain metrics, weighted square error and spectral slope, were found to work best. The computed metric values were used to train and verify the performance of the artificial neural network (ANN), which was able to achieve 100% accuracy on training data and 100% accuracy on verification data. [This research was supported wholly by the U.S. Department of Defense, through the Strategic Environmental Research and Development Program (SERDP).]

New Nonlinear Filtering Strategies for Eliminating Medium and Long Tailed Noise in Images with Edge Preservation Properties

Arithmetic filters are quite effective in recovering the images confounded by medium-tailed (Gaussian) noise. These filters, however, perform poorly in the presence of additive long-tailed (impulse) noise and they do not preserve edge structures. Median smoother discards outliers (impulses) effectively, but it fails to provide adequate smoothing for images corrupted with non-impulsive noise. In this paper, two new nonlinear techniques for image filtering, which we call New Filter I and New Filter II, are proposed based on a recently developed nonlinear highpass filter algorithm. New Filter I suppresses Gaussian noise quite well. New Filter II effectively eliminates impulses and filters out noise having Gaussian statistics. Both the Alters are shown to have good edge preserving characteristics. The proposed filters are evaluated for their performance on a test image and the results obtained are included.

Subspace-based frequency estimation of sinusoidal signals in alpha-stable noise

In the frequency estimation of sinusoidal signals observed in impulsive noise environments, techniques based on Gaussian noise assumption are unsuccessful. One possible way to find better estimates is to model the noise as an alpha-stable process and to use the fractional lower order statistics (FLOS) of the data to estimate the signal parameters. In this work, we propose a FLOS-based statistical average, the generalized covariation coefficient (GCC). The GCCs of multiple sinusoids for unity moment order in S αS noise attain the same form as the covariance expressions of multiple sinusoids in white Gaussian noise. The subspace-based frequency estimators FLOS–multiple signal classification (MUSIC) and FLOS–Bartlett are applied to the GCC matrix of the data. On the other hand, we show that the multiple sinusoids in S αS noise can also be modeled as a stable autoregressive moving average process approximated by a higher order stable autoregressive (AR) process. Using the GCCs of the data, we obtain FLOS versions of Tufts–Kumaresan (TK) and minimum norm (MN) estimators, which are based on the AR model. The simulation results show that techniques employing lower order statistics are superior to their second-order statistics (SOS)-based counterparts, especially when the noise exhibits a strong impulsive attitude. Among the estimators, FLOS–MUSIC shows a robust performance. It behaves comparably to MUSIC in non-impulsive noise environments, and both in impulsive and non-impulsive high-resolution scenarios. Furthermore, it offers a significant advantage at relatively high levels of impulsive noise contamination for distantly located sinusoidal frequencies.

A robust approach to image enhancement based on fuzzy logic

In this paper, we propose a robust approach to image enhancement based on fuzzy logic that addresses the seemingly conflicting goals of image enhancement: (i) removing impulse noise, (ii) smoothing out nonimpulse noise, and (iii) enhancing (or preserving) edges and certain other salient structures. We derive three different filters for each of the above three tasks using the weighted (or fuzzy) least squares (LS) method, and define the criteria for selecting each of the three filters. The criteria are based on the local context, and they constitute the antecedent clauses of the fuzzy rules. The overall result of the fuzzy rule-based system is the combination of the results of the individual filters, where each result contributes to the degree that the corresponding antecedent clause is satisfied. This approach gives us a powerful and flexible image enhancement paradigm. Results of the proposed method on several types of images are compared with those of other standard techniques.

Noise compensation methods for hidden Markov model speech recognition in adverse environments

Several noise compensation schemes for speech recognition in impulsive and nonimpulsive noise are considered. The noise compensation schemes are spectral subtraction, HMM-based Wiener (1949) filters, noise-adaptive HMMs, and a front-end impulsive noise removal. The use of the cepstral-time matrix as an improved speech feature set is explored, and the noise compensation methods are extended for use with cepstral-time features. Experimental evaluations, on a spoken digit database, in the presence of ear noise, helicopter noise, and impulsive noise, demonstrate that the noise compensation methods achieve substantial improvement in recognition across a wide range of signal-to-noise ratios. The results also show that the cepstral-time matrix is more robust than a vector of identical size, which is composed of a combination of cepstral and differential cepstral features.

Suppression of Impulsive Noise in FM via Rank Order Processing

Suppression of impulsive non-Gaussian noise in FM, via rank order signal processing methods, appears feasible in circumstances whose precise limits, particularly in terms of bandwidth and levels of nonimpulsive noise, remain to be determined. The methods are related to median filtering, but are elaborated in the sense of admitting more operations on the signal values. The application is feasible because demodulatian of FM requires only zero crossing information, and because rank order processors can be defined whose root signals have the same sign changes as the FM signals admitted.

Nonlinear scale-space filtering and multiresolution system

We derive and demonstrate a nonlinear scale-space filter and its application in generating a nonlinear multiresolution system. For each datum in a signal, a neighborhood of weighted data is used for clustering. The cluster center becomes the filter output. The filter is governed by a single scale parameter that dictates the spatial extent of nearby data used for clustering. This, together with the local characteristic of the signal, determines the scale parameter in the output space, which dictates the influences of these data on the output. This filter is thus adaptive and data driven. It provides a mechanism for (a) removing impulsive noise, (b) improved smoothing of nonimpulsive noise, and (c) preserving edges. Comparisons with Gaussian scale-space filtering and median filters are made using real images. Using the architecture of the Laplacian pyramid and this nonlinear filter for interpolation, we construct a nonlinear multiresolution system that has two features: (1) edges are well preserved at low resolutions, and (2) difference signals are small and spatially localized. This filter implicitly presents a new mechanism for detecting discontinuities differing from techniques based on local gradients and line processes. This work shows that scale-space filtering, nonlinear filtering, and scale-space clustering are closely related and provides a framework within which further image processing, image coding, and computer vision problems can be investigated.

A robust nonlinear filter for image restoration

A class of nonlinear regression filters based on robust estimation theory is introduced. The goal of the filtering is to recover a high-quality image from degraded observations. Models for desired image structures and contaminating processes are employed, but deviations from strict assumptions are allowed since the assumptions on signal and noise are typically only approximately true. The robustness of filters is usually addressed only in a distributional sense, i.e., the actual error distribution deviates from the nominal one. In this paper, the robustness is considered in a broad sense since the outliers may also be due to inappropriate signal model, or there may be more than one statistical population present in the processing window, causing biased estimates. Two filtering algorithms minimizing a least trimmed squares criterion are provided. The design of the filters is simple since no scale parameters or context-dependent threshold values are required. Experimental results using both real and simulated data are presented. The filters effectively attenuate both impulsive and nonimpulsive noise while recovering the signal structure and preserving interesting details.

Adaptive median filters: new algorithms and results

Based on two types of image models corrupted by impulse noise, we propose two new algorithms for adaptive median filters. They have variable window size for removal of impulses while preserving sharpness. The first one, called the ranked-order based adaptive median filter (RAMF), is based on a test for the presence of impulses in the center pixel itself followed by a test for the presence of residual impulses in the median filter output. The second one, called the impulse size based adaptive median filter (SAMF), is based on the detection of the size of the impulse noise. It is shown that the RAMF is superior to the nonlinear mean L(p) filter in removing positive and negative impulses while simultaneously preserving sharpness; the SAMF is superior to Lin's (1988) adaptive scheme because it is simpler with better performance in removing the high density impulsive noise as well as nonimpulsive noise and in preserving the fine details. Simulations on standard images confirm that these algorithms are superior to standard median filters.

Multilevel nonlinear filters for edge detection and noise suppression

Two new classes of multilevel nonlinear filters are introduced for simultaneous edge detection and noise suppression, which the authors call a nested median filter/median averaging filter (NMF/MAF) pair and a delayed decision filter/embedded median trimmed filter (DDF/EMTF) pair. Median filters and median-related filter cause an edge shift in the presence of an impulse near the edge. The proposed filters reduce such edge shifting while suppressing impulsive as well as nonimpulsive noise. It is shown that at the noisy edge point the NMF and the DDF are substantially superior both theoretically and experimentally to the median filter, the /spl alpha/-TM filter, and the STM filter in two respects: (1) the output bias error and (2) the output mean square error. It is also shown that in the noisy homogeneous region (nonedge point), the bias errors of the MAF are zero and the output mean square errors of the MAF are substantially close to those of the optimized single-level filters: the averager, the median filter, and the min-max filter under Gaussian, Laplacian, and uniform noise, respectively. Test results confirm that the NMF/MAF pair and the DDF/EMTF structure are each robust in preserving sharp edges, inhibiting edge shifting, and suppressing a wide variety of noise. >