Showing papers on "Noise reduction published in 1996"

Speech enhancement based on a priori signal to noise estimation

Abstract: This paper addresses the problem of single microphone frequency domain speech enhancement in noisy environments. The main characteristics of available frequency domain noise reduction algorithms are presented. We have confirmed that the a priori SNR estimation leads to the best subjective results. According to these conclusions, a new approach is then developed which achieves a trade-off between effective noise reduction and low computational load for real-time operations. The obtained solutions demonstrate that the subjective and objective results are much better than existing methods.

Robust continuous speech recognition using parallel model combination

Abstract: This paper addresses the problem of automatic speech recognition in the presence of interfering noise. It focuses on the parallel model combination (PMC) scheme, which has been shown to be a powerful technique for achieving noise robustness. Most experiments reported on PMC to date have been on small, 10-50 word vocabulary systems. Experiments on the Resource Management (RM) database, a 1000 word continuous speech recognition task, reveal compensation requirements not highlighted by the smaller vocabulary tasks. In particular, that it is necessary to compensate the dynamic parameters as well as the static parameters to achieve good recognition performance. The database used for these experiments was the RM speaker independent task with either Lynx Helicopter noise or Operation Room noise from the NOISEX-92 database added. The experiments reported here used the HTK RM recognizer developed at CUED modified to include PMC based compensation for the static, delta and delta-delta parameters. After training on clean speech data, the performance of the recognizer was found to be severely degraded when noise was added to the speech signal at between 10 and 18 dB. However, using PMC the performance was restored to a level comparable with that obtained when training directly in the noise corrupted environment.

Monte Carlo SSA: Detecting irregular oscillations in the Presence of Colored Noise

Abstract: Singular systems (or singular spectrum) analysis (SSA) was originally proposed for noise reduction in the analysis of experimental data and is now becoming widely used to identify intermittent or modulated oscillations in geophysical and climatic time series. Progress has been hindered by a lack of effective statistical tests to discriminate between potential oscillations and anything but the simplest form of noise, that is, “white” (independent, identically distributed) noise, in which power is independent of frequency. The authors show how the basic formalism of SSA provides a natural test for modulated oscillations against an arbitrary “colored noise” null hypothesis. This test, Monte Carlo SSA, is illustrated using synthetic data in three situations: (i) where there is prior knowledge of the power-spectral characteristics of the noise, a situation expected in some laboratory and engineering applications, or when the “noise” against which the data is being tested consists of the output of an independently specified model, such as a climate model; (ii) where a simple hypothetical noise model is tested, namely, that the data consists only of white or colored noise; and (iii) where a composite hypothetical noise model is tested, assuming some deterministic components have already been found in the data, such as a trend or annual cycle, and it needs to be established whether the remainder may be attributed to noise. The authors examine two historical temperature records and show that the strength of the evidence provided by SSA for interannual and interdecadal climate oscillations in such data has been considerably overestimated. In contrast, multiple inter- and subannual oscillatory components are identified in an extended Southern Oscillation index at a high significance level. The authors explore a number of variations on the Monte Carlo SSA algorithm and note that it is readily applicable to multivariate series, covering standard empirical orthogonal functions and multichannel SSA.

Wind Turbine Noise

Abstract: 1 Introduction.- 1.1 Current Situation of Wind Energy and Perspectives.- 1.2 Advantages of Wind Energy.- 1.3 Current Problems of Wind Energy.- 1.4 Road Map of the Book.- 2 Noise and its Effects.- 2.1 Sound and Noise.- 2.2 Definitions.- 2.3 Noise Regulations.- 3 Introduction to Aeroacoustics.- 3.1 Introduction.- 3.2 Definitions.- 3.3 The Linear Wave Equation.- 3.4 Elementary Solutions of the Wave Equation.- 3.5 Lighthill's Acoustic Analogy.- 3.6 The Influence of Boundaries.- 3.7 Application of Aeroacoustic Theory.- 3.8 Conclusions.- 4 Noise Mechanisms of Wind Turbines.- 4.1 Classification of Noise Mechanisms.- 4.2 Low-Frequency Noise.- 4.3 Inflow-Turbulence Noise.- 4.4 Airfoil Self-Noise.- 4.5 Summary.- 5 Noise Prediction.- 5.1 Introduction.- 5.2 Low-Frequency Noise.- 5.3 High-Frequency Noise.- 5.4 Summary.- 6 Noise Propagation.- 6.1 Introduction.- 6.2 Mechanisms.- 6.3 Prediction.- 6.4 Results.- 6.5 Summary.- 7 Measurement of Noise and Flow Field.- 7.1 Acoustic Measurement in the Wind Tunnel.- 7.2 Acoustic Measurements on Operating Turbines.- 7.3 Flow Visualization on Operating Turbines.- 8 Noise Reduction.- 8.1 Introduction.- 8.2 Reduction of Tip Speed and Angle of Attack.- 8.3 Reduction of Trailing-Edge Noise.- 8.4 Reduction of Tip Noise.- 8.5 Reduction of Inflow-Turbulence Noise.- 8.6 Reduction of Blunt-Trailing-Edge Noise.- 8.7 Conclusions.- 9 Future Work.- 10 References.- 10.1 Recommended References.- 10.2 List of References.- Annex: Description of Tip Planform Shapes.

Design of an extended Kalman filter frequency tracker

Abstract: The design of an extended Kalman filter for tracking a time-varying frequency is discussed. Its principal modes of failure are explained. The design tradeoff between balancing noise rejection and tracking at a maximal slew rate is discussed. The performance penalties for overdesign and underdesign of noise covariances are examined, and theoretically supported design guidelines are suggested.

Reliably estimating the noise in AVIRIS hyperspectral images

Abstract: A new method is presented for computing the noise affecting each band of an AVIRIS hyperspectral image. Between-band (spectral) and within-band (spatial) correlations are used to decorrelate the image data via linear regression. Each band of the image is divided into small blocks, each of which is independently decorrelated. The decorrelation leaves noise-like residuals whose variance estimates the noise. A homogeneous set of these variances is selected and their values are combined to provide the best estimate of that band's noise. This method provides consistent noise estimates from images with very different land cover types. Its performance is validated by comparing its noise estimates with noise measures provided with two AVIRIS images. The method works well with inhomogeneous images (e.g., of a vegetated area such as Jasper Ridge) unlike a method described recently by Gao. The method is automatic and does not require the intervention of a human operator. Noise estimates are presented for 10...

A unified approach to noise removal, image enhancement, and shape recovery

Abstract: We present a unified approach to noise removal, image enhancement, and shape recovery in images. The underlying approach relies on the level set formulation of the curve and surface motion, which leads to a class of PDE-based algorithms. Beginning with an image, the first stage of this approach removes noise and enhances the image by evolving the image under flow controlled by min/max curvature and by the mean curvature. This stage is applicable to both salt-and-pepper grey-scale noise and full-image continuous noise present in black and white images, grey-scale images, texture images, and color images. The noise removal/enhancement schemes applied in this stage contain only one enhancement parameter, which in most cases is automatically chosen. The other key advantage of our approach is that a stopping criteria is automatically picked from the image; continued application of the scheme produces no further change. The second stage of our approach is the shape recovery of a desired object; we again exploit the level set approach to evolve an initial curve/surface toward the desired boundary, driven by an image-dependent speed function that automatically stops at the desired boundary.

Coding parameter adaptive transform artifact reduction process

Abstract: A post-processor for a decoded video sequence includes a digital noise reduction unit and an artifact reduction unit which significantly reduce blocking artifacts and mosquito noise in a video image. The post-processor uses both temporal and edge characteristics of the video image to enhance the displayed image. A coding parameter from a decoder is used in a coding parameter adaptive filter unit within an artifact unit to further enhance the perceived quality of the displayed image. The coding parameter for a particular macroblock is selected using a characteristic of that macroblock. The post-processor operates on a current frame of pixel data using information from the immediately preceding post-processed frame that is stored in a frame memory of the post-processor. The post-processor uses artifact reduction only on portions of the image that are not part of an edge, and are not part of a texture or fine detail area. Since artifact reduction is not utilized on these areas, the post-processed image is not softened in regions where it is easily noticed by the human eye.

Image recovery using the anisotropic diffusion equation

Abstract: A new approach for image recovery using the anisotropic diffusion equation is developed which is based on the first derivative of the signal in time embedded in family of images with different scales. The diffusion coefficient is determined as a function of the gradient of the signal convolved with a symmetric exponential filter. A new discrete realization is developed for the simultaneous removal of noise and preservation of edges.

Speckle noise reduction in television holography fringes using wavelet thresholding

Abstract: Recently, a novel wavelet method to reduce speckle noise in synthetic aperture radar images was presented. The method, based on the thresholding of the wavelet coefficients of the transformed image, is computationally efficient and maintains sharp image features. The application of a similar method is explored to reduce speckle noise in TV holography fringes, which is a very important problem in the quantitative analysis of transient phenomena. Several thresholding approaches are used to test the noise reduction algorithm on computer-simulated fringes and results are assessed through the evaluation of two comparative parameters: the image fidelity and the speckle index. It is shown that the method achieves the highest speckle reduction when speckle grains have the same average size of the image pixels.

On-board phase and modulus calibration of large aperture synthesis radiometers: study applied to MIRAS

Abstract: On-board calibration of bidimensional aperture synthesis radiometers with a large number of antennas by the standard correlated noise injection method is technologically very critical because of the stringent requirements on mass, volume, and phase equalization of the noise distribution network. A novel approach, which makes use of a set of uncorrelated noise sources uniformly distributed in the array, is proposed. Each noise source drives correlated noise only to a small set of adjacent antennas. These sets of antennas are overlapped in order to maintain phase and modulus track along the array. This approach reduces drastically mass and volume of the noise distribution network. Moreover, its phase matching requirement is strongly relaxed because it is only necessary within small sets of adjacent antennas. Power stability of the uncorrelated noise sources is also not a stringent requirement. This procedure allows independent phase and modulus calibration by making use of a reduced number of redundant correlations.

Anisotropic spectral magnitude estimation filters for noise reduction and image enhancement

Abstract: Describes an algorithm for noise reduction and enhancement of images which is able to take into account anisotropies of signal as well as of noise. Processing is based on subjecting each image to a block DFT, followed by comparing each observed magnitude coefficient to the expected noise standard deviation for it. Depending on this comparison, each coefficient is attenuated the more, the more likely it is that it contains only noise. In addition, the attenuation is made dependent on whether or not the observed coefficient contributes to an oriented prominent structure within the processed image block. Orientation as well as the distinctness with which it occurs are detected in the spectral domain by an inertia-like matrix. Orientation information is additionally exploited to selectively enhance oriented structures, thus only marginally increasing noise as compared to isotropic enhancement.

A transform artifact reduction process

Abstract: A post-processor for a decoded video sequence includes a digital noise reduction unit and an artifact reduction unit which significantly reduce blocking artifacts and mosquito noise in a video image. The post-processor uses both temporal and edge characteristics of the video image to enhance the displayed image. The post-processor operates on a current frame of pixel data using information from the immediately preceding post-processed frame that is stored in a frame memory of the post-processor. The post-processor first identifies texture and fine detail areas in the image. The post-processor uses artifact reduction only on portions of the image that are not part of an edge, and are not part of a texture or fine detail area. Since artifact reduction is not utilized on these areas, the post-processed image is not softened in regions where it is easily noticed by the human eye.

Adaptive LMS L-filters for noise suppression in images

Abstract: Several adaptive least mean squares (LMS) L-filters, both constrained and unconstrained ones, are developed for noise suppression in images and compared in this paper. First, the location-invariant LMS L-filter for a nonconstant signal corrupted by zero-mean additive white noise is derived. It is demonstrated that the location-invariant LMS L-filter can be described in terms of the generalized linearly constrained adaptive processing structure proposed by Griffiths and Jim (1982). Subsequently, the normalized and the signed error LMS L-filters are studied. A modified LMS L-filter with nonhomogeneous step-sizes is also proposed in order to accelerate the rate of convergence of the adaptive L-filter. Finally, a signal-dependent adaptive filter structure is developed to allow a separate treatment of the pixels that are close to the edges from the pixels that belong to homogeneous image regions.

Adaptive scheme for elimination of broadband noise and impulsive disturbances from AR and ARMA signals

Abstract: The problem of elimination of measurement noise and impulsive disturbances from autoregressive and autoregressive moving average signals is considered. It is shown that the task of simultaneous detection/tracking/restoration can be stated as a nonlinear filtering problem and solved using the theory of extended Kalman filter. Numerical tests carried out for real audio signals corrupted by both real and artificially generated disturbances confirm very good properties of the proposed algorithm.

Combination of two-channel spectral subtraction and adaptive wiener post-filtering for noise reduction and dereverberation

Abstract: In this contribution a novel structure for the enhancement of speech signals disturbed by acoustic noise is presented which is based on Spectral Subtraction. The Spectral Subtraction technique is combined with a novel estimator for the noise power spectrum which takes advantage of the employment of a second microphone. Due to the extension to a two-microphone system the Spectral Subtraction can be used to reduce realistic, non-stationary noise sources. Additionally, the performance of the system is further improved by the application of a post filter adapted according to Wiener filter techniques. As a result, the proposed speech enhancement system provides a significant suppression of noise in realistic situations as well as a reduction of room reverberation.

Biased motion-adaptive temporal filtering for speckle reduction in echocardiography

Abstract: Describes a new fully motion-adaptive spatio-temporal filtering technique to reduce the speckle in ultrasound images. The advantages of this approach are demonstrated in echocardiographic boundary detection and in comparison with other techniques. The first stage of many automated echocardiographic image interpretation schemes is filtering to reduce the amount of speckle noise. The authors show how the two-dimensional least mean squares (TDLMS) filter can be configured as a motion-compensated filter for a time sequence of ultrasound images that eliminates the blurring associated with direct averaging. For an image corrupted by multiplicative speckle noise, the mode of the intensity distribution approximates the maximum likelihood estimator. In consequence, the temporal filter's output is biased towards the mode from the mean, using information contained within the speckle itself. A new adaptive algorithm for controlling the filter's convergence is also included. To evaluate performance, application to simulated, phantom, and an in vivo test sequence of the carotid artery are considered in comparison with other techniques. The effect of filtering on edges is of great importance, as these are used by subsequent image interpretation schemes. Quantitative measurements demonstrate the effectiveness of the Biased TDLMS filter, for both noise reduction and edge preservation. Echocardiographic images have a high noise content and suffer from poor contrast. Despite this challenging environment, the Biased TDLMS filter is shown to produce images that are better inputs for subsequent feature extraction. The benefits for echocardiographic images are highlighted by considering the problems of mitral valve analysis and extraction of the left atrium boundary.

Analysis of regularized total variation penalty methods for denoising

Abstract: The problem of recovering images with sharp edges by total variation denoising has recently received considerable attention in image processing. Numerical difficulties in implementing this nonlinear filter technique are partly due to the fact that it involves the stable evaluations of unbounded operators. To overcome that difficulty we propose to approximate the evaluation of the unbounded operator by a stable approximation. A convergence analysis for this regularized approach is presented.

Adaptive noise removal from biomedical signals using warped polynomials

Abstract: Presents the time-warped polynomial filter (TWPF), a new interval-adaptive filter for removing stationary noise from nonstationary biomedical signals. The filter fits warped polynomials to large segments of such signals. This can be interpreted as low-pass filtering with a time-varying cutoff frequency. In optimal operation, the filter's cut-off frequency equals the local signal bandwidth. However, the author also presents an iterative filter adaptation algorithm, which does not rely on the (complicated) computation of the local bandwidth. The TWPF has some important advantages over existing adaptive noise removal techniques: it reacts immediately to changes in the signal's properties, independently of the desired noise reduction; it does not require a reference signal and can be applied to nonperiodical signals. In case of quasiperiodical signals, applying the TWPF to the individual signal periods leads to an optimal noise reduction. However, the TWPF can also be applied to intervals of fixed size, at the expense of a slightly lower noise reduction. This is the way nonquasiperiodical signals are filtered. The author presents experimental results which demonstrate the usefulness of the interval-adaptive filter in several biomedical applications: noise removal from ECG, respiratory and blood pressure signals, and base-line restoration of electroencephalograms (EEGs).

De-noising and contrast enhancement via wavelet shrinkage and nonlinear adaptive gain

Abstract: This paper presents an approach which addresses both de-noising and contrast enhancement. In a multiscale wavelet analysis framework, we take advantage of both soft thresholding and hard thresholding wavelet shrinkage techniques to reduce noise. In addition, we carry out nonlinear processing to enhance contrast within structures and along boundaries. Feature restoration and enhancement are accomplished by modifying the gain of a signal's variational energy. The multiscale discrete dyadic wavelet transform adapted in this paper is treated as a process for the diffusion of variational energy from a signal stored as the power (scaled variational energy) of wavelet coefficients. We show that a discrete dyadic wavelet transform has the capability to separate feature variational energy from noise variational energy. De- noising and feature enhancement are achieved by simultaneously lowering noise variational energy and raising feature variational energy in the transform domain. We present methods for achieving this objective, including regulated soft thresholding and adaptive nonlinear processing combined with hard thresholding. We have applied this algorithm to synthetic and real signals as well as images with additive Gaussian white noise. Experimental results show that de-noised as well as enhanced signals and images are free from artifacts. Sample analysis and experimental results are presented.

An adaptive nearest neighbor multichannel filter

Abstract: This paper addresses the problem of noise attenuation for multichannel data. The proposed filter utilizes adaptively determined data-dependent coefficients based on a novel distance measure which combines vector directional with vector magnitude filtering. The special case of color image processing is studied as an important example of multichannel signal processing.

Noise reduction in an image

Abstract: In a method of processing an image, the image is divided in one or more blocks. Separate blocks are spatially frequency transformed in that pixel-values of said blocks are transformed into spectral coefficients. A noise level of the image is estimated and reduced spectral coefficients are derived from spectral coefficients and the estimated noise level. Pixel-values for a processed block are synthesized from the reduced spectral coefficients and the processed blocks are assembled into a processed image. The noise level is estimated from the image information within the image. Preferably, a few parameters relating to the circumstances under which the image was acquired are also taken into account for estimating the noise level.

Speckle noise reduction of airborne SAR images with symmetric Daubechies wavelets

Abstract: We report the study of a multiresolution speckle reduction method for airborne synthetic aperture radar (SAR) images. The SAR image is first subband-coded using complex symmetric Daubechies wavelets, followed by a noise estimate on the three high-pass bands. An elliptic wavelet coefficient thresholding rule is then applied, that preserves the global orientation of the complex wavelet coefficient distribution. FInally, a multiresolution synthesis (inverse wavelet transform) is done in a last small dim objects. A speckle index is computed to quantify the speckle reduction performance. We compare our results with those obtained using median and geometrical (Crimmins) filters.

Influence of modeling error on noise reduction performance of active noise control systems using filtered-X LMS algorithm

Abstract: This paper investigates the influence of modeling error that is the difference of the characteristic between the secondary path and its model on the behavior of the filtered-X LMS adaptive filter. The equations that describe the behavior of the adaptive filter are presented at first, and the influence of modeling error is considered based on the equations. Both the stability and the noise reduction performance are discussed, and particularly, we discussed in detail the noise reduction performance under modeling error condition that has rarely been considered. In addition, the conditions required for maintaining the noise reduction performance is also presented. The results of the theoretical consideration are confirmed by computer simulation in which the impulse responses measured in a vehicle cabin are used. Through the investigation, it is proved that the algorithm may be stable except for the substantial modeling error case, and the noise reduction performance will be inferior to that under ideal condition in general. These results suggest that we should use the on-line identification system when the secondary path characteristic is time variant.

Noise filtering in solar speckle masking reconstructions

Abstract: A new method of obtaining a sensitive noise filter for solar speckle masking reconstructions is presented below. This filter separates the true image information from noise most reliably. Its efficiency is demonstrated by some representative examples considering observed and artificial image data which were generated in a computer. The latter set of data also suffered realistic degradations by the influence of seeing and noise taken from suitable observations.

Basics of analog differential filters

Abstract: We describe and analyze passive and active analog filters with differential input and differential output. They are implemented by coupling single-ended filters and provide very high common-mode rejection ratios. This makes it possible to place these filters before differential amplifiers, thus improving interference rejection and noise reduction.