Showing papers on "Noise (signal processing) published in 1988"

Signal enhancement-a composite property mapping algorithm

Abstract: A signal enhancement algorithm is developed that seeks to recover a signal from noise-contaminated distorted measurements made on that signal. This object is achieved by utilizing a set of properties which the signal is known or is hypothesized as possessing. The measured signal is modified to the smallest degree necessary to sequentially possess each of the individual properties. Conditions for the algorithm's convergence are established in which the primary requirement is that a composite property mapping be closed. This is a relatively unrestricted condition in comparison to that required of most existing signal-enhancement algorithms. >

Matched field processing: source localization in correlated noise as an optimum parameter estimation problem

Abstract: Matched field processing is a parameter estimation technique for localizing the range, depth, and bearing of a point source from the signal field propagating in an acoustic waveguide. The signal is observed at an array in the presence of additive, spatially correlated noise that also propagates in the same ocean environment as the signal. In a weak signal‐to‐noise situation this parameter estimation requires the maximum exploitation of the physics of both the signal and noise structure which then must be coupled to optimum methods for the signal processing. We study the physics of this processing by modeling the ocean environment as a waveguide that is horizontally stratified with an arbitrary sound‐speed profile in the vertical. Thus, the wave equation describes the underlying structure of the signal and noise, and the signal processing via the generation of the replica fields. Two methods of array processing are examined: (i) the linear cross correlator (Bartlett) and (ii) the maximum likelihood method ...

Low-noise techniques in detectors

Abstract: We concentrate in this review on the fundamentals of low-noise techniques and on understanding the limits in the charge signal measurements. Charge collection and signal formation in detectors, the origin and properties of noise, noise and amplification of charge, and detectors and signal processing are discussed. (AIP)

A microphone array with adaptive post-filtering for noise reduction in reverberant rooms

Abstract: The author presents a self-adapting noise reduction system which is based on a four-microphone array combined with an adaptive postfiltering scheme. Noise reduction is achieved by utilizing the directivity gain of the array and by reducing the residual noise through postfiltering of the received microphone signals. The postfiltering scheme depends on a Wiener filter estimating the desired speech signal and is computed from short-term measurements of the autocorrelation and cross-correlation functions of the microphone signals. The noise reduction system has been tested experimentally in a typical office room. The system produces an enhanced speech signal with barely noticeable residual noise if the input SNR is greater than 0 dB. The received noise power-measured in the absence of the speech signal-can be reduced by 28 dB. >

Adaptive eigensubspace algorithms for direction or frequency estimation and tracking

Abstract: The authors present an adaptive estimator of the complete noise or signal subspace of a sample covariance matrix as well as the estimator's practical implementations. The general formulation of the proposed estimator results from an asymptotic argument, which shows the signal or noise subspace computation to be equivalent to a constrained gradient search procedure. A highly parallel algorithm, denoted the inflation method, is introduced for the estimation of the noise subspace. The simulation results of these adaptive estimators show that the adaptive subspace algorithms perform substantially better than P.A. Thompson's (1980) adaptive version of V.F. Pisarenko's technique (1973) in estimating frequencies or directions of arrival (DOA) of plane waves. For tracking nonstationary parameters, the simulation results also show that the adaptive subspace algorithms are better than direct eigendecomposition methods for which computational complexity is much higher than the adaptive versions. >

Median filters with adaptive length

Abstract: Two algorithms using adaptive-length median filters are proposed for improving impulse-noise-removal performance for image processing. The algorithms can achieve significantly better image quality than regular (fixed-length) median filters when the images are corrupted by impulse noise. One of the algorithms, when realized in hardware, requires rather simple additional circuitry. Both algorithms can easily be integrated into efficient hardware realizations for median filters. The performance of the proposed filters is compared with regular median filters, generalized mean filters, and nonlinear mean filters. The hardware complexities of the filters are also compared. >

An Application of the Principle of Maximum Information Preservation to Linear Systems

Abstract: This paper addresses the problem of determining the weights for a set of linear filters (model "cells") so as to maximize the ensemble-averaged information that the cells' output values jointly convey about their input values, given the statistical properties of the ensemble of input vectors The quantity that is maximized is the Shannon information rate, or equivalently the average mutual information between input and output Several models for the role of processing noise are analyzed, and the biological motivation for considering them is described For simple models in which nearby input signal values (in space or time) are correlated, the cells resulting from this optimization process include center-surround cells and cells sensitive to temporal variations in input signal

A parametric method of identification of single-trial event-related potentials in the brain

Abstract: A parametric method of identification of event-related (or evoked) potentials on a single-trial basis through an ARX (autoregressive with exogenous input) algorithm is discussed. The basic estimation of the information contained in the single trial is taken from an average carried out on a sufficient number of trials, while the noise sources, EEG and EOG, are characterized as exogenous inputs in the model. The simulations as well as the experimental results confirm the capability of the model of drastically improving the S/N (signal-to-noise) ratio in each single trial and satisfactorily identifying the contributions of signal and noise to the overall recording. A particularly efficient reduction of ocular artifacts is also achieved. >

Passive direction-of-arrival and range estimation for near-field sources

Abstract: An algorithm for simultaneously estimating the range and bearing of multiple near-field sources is presented. The method is based on the application of signal subspace ideas to the spatial Wigner-Ville distribution approach originally presented by B.R. Breed and T.E. Posch (Proc. ICASSP'84, p.41B.9.1, 1984). The principal advantages of using signal-subspace methods are that the range/direction-of-arrival estimates are obtained with improved precision and resolution, and without computation or search of a complicated spectral surface. Additionally, these methods allow a simple and more effective extension of the spatial Wigner-Ville approach to cases in which noise and/or multiple signals are present. Simulations have shown that the algorithm performs well for a wide variety of test cases, and comparisons with the Cramer-Rao bound indicate near-optimal direction-of-arrival estimates. >

A method for indicating the presence of speech in an audio signal

Abstract: A voice operated switch employs digital signal processing techniques to examine audio signal frames having harmonic content to identify voiced phonemes and to determined whether the signal frame contains primarily speech or noise. The method and apparatus employ a multiple-stage, delayed-decision adaptive digital signal processing algorithm implemented through the use of commonly available electronic circuit components. Specifically the method and apparatus comprise a plurality of stages, including (1) a low-pass filter to limit examination of input signals to below about one kHz, (2) a digital center-clipped autocorrelation processor whih recognizes that the presence of periodic components of the input signal below and above a peak-related threshold identifies a frame as containing speech or noise, and (3) a nonlinear filtering processor which includes nonlinear smoothing of the frame-level decisions and incorporates a delay, and further incorporates a forward and backward decision extension at the speech-segment level of several tenths of milliseconds to determine whether adjacent frames are primarily speech or primarily noise.

Digital Signal Processing: A System Design Approach

Abstract: Introduction to Digital Signal Processing Discrete-Time Signal Analysis and Linear Systems Linear Time-Invariant Digital Systems The Z-Transform Infinite Impulse Response Digital Filter Design The Discrete Fourier Transform and Fast Fourier Transform Algorithms Multirate Digital Signal Processing Response of Linear Systems to Discrete-Time Random Processes, Power Spectrum Estimation, and Detection of Signals in Noise Finite Register Length Effects in Digital Signal Processing Signal Processing System Design Adaptive Filtering.

Single-user detectors for multiuser channels

Abstract: Optimum decentralized demodulation for asynchronous Gaussian multiaccess channels is considered. It is assumed that the receiver is the destination of the information transmitted by only one active user, and single-user detectors that take into account the existence of the other active users in the channel are obtained. The problem considered is one of signal detection in additive colored nonGaussian noise, and attention is focused on one-shot structures where detection of each symbol is based only on the received process during its corresponding interval. Particular emphasis is placed on asymptotically optimum detectors for weak interferers, for CDMA (code-division multiple-access) signature waveforms with long spreading codes, and for low background Gaussian noise level. >

A comparative study and assessment of Doppler ultrasound spectral estimation techniques. Part II: Methods and results.

Abstract: Various alternative spectral estimation methods are examined and compared in order to assess their possible application for real-time analysis of Doppler ultrasound arterial signals. Specifically, five general frequency domain models are examined, including the periodogram, the general autoregressive moving average (ARMA) model which has the autoregressive (AR) and moving average (MA) models as special cases, and Capon's maximum likelihood spectral model. A simulated stationary Doppler signal with a known theoretical spectrum was used as the reference test sequence, and white noise was added to enable various signal/noise conditions to be created. The performance of each method representative of each spectral model was assessed using both qualitative and quantitative schemes that convey information related to the bias and variance of the spectral estimates. Three integrated performance indices were implemented for quantitative analysis. The relative computational complexity for each algorithm was also investigated. Our results indicate that both the AR(Yule-Walker) and ARMA(singular value decomposition) models of orders (8) and (4, 4), respectively, show good agreement with the theoretical spectrum, and yield estimates with variances considerably less than the Fast Fourier Transform (FFT). Preliminary results obtained with these methods using a clinical, non-stationary Doppler signal supports these observations.

Transform-ratio ternary phase-amplitude filter formulation for improved correlation discrimination

Abstract: A method of formulating ternary-valued (-1,0,1) correlation filters based on the ratio of spectral energies of target and nontarget patterns is proposed, and performance of such filters is investigated by computer simulations of correlation. The results confirm the intuitive expectation that such filters can enhance signalto- clutter and discrimination performance for target recognition in the presence of large amounts of input noise. These filters may be viewed as an extension of binary phase-only filters and similarly are motivated by the prospect of near-term real-time implementation.

Optimum beamforming for coherent signal and interferences

Abstract: An approach is described to optimum beamforming in the presence of correlated interferences completely overcoming the signal cancellation phenomenon. In contrast with classical adaptive beamforming where no assumptions are made on the statistical properties of the interference and noise, other than their being uncorrelated with the desired signal, the present approach is based on an explicit statistical model of the signal environment. The method is adaptive, however, in that the characterization of the signal environment is extracted from the measured array data. >

Comparison of four digital maximum frequency estimators for Doppler ultrasound.

Abstract: The performance of four methods for digitally estimating the maximum frequency waveform from the Doppler ultrasound spectrum, are described. The methods investigated are: a percentile method, D'Alessio's threshold crossing method [D'Alessio T. (1985) "Objective" algorithm for maximum frequency estimation in Doppler spectral analysers. Med. Biol. Engng and Comput. 23, 63-68.], a modified threshold crossing method, and a new hybrid algorithm. Evaluations of the variance and bias were performed using stationary simulated continuous wave (CW) Doppler signals of different bandwidths and signal/noise ratios (SNR) of 9 and 17 dB. Furthermore, a simulated nonstationary Doppler signal, similar to that from a normal internal carotid artery, was also used to compare the various methods. Overall, it was found that the modified threshold method and the new hybrid method have the best performance over a wide range of signal and noise hybrid method have the best performance over a wide range of signal and noise conditions; however, D'Alessio's method also performs well for low SNR's.

Image processor for enhancing contrast between subregions of a region of interest

Abstract: An imaging technique is disclosed for enhancing the contrast of an image, in particular for enhancing the contrast between subregions of a region of interest which may have similar signal characteristics and significantly distinct physical properties. A distribution density function describing the density of the signal in a signal space assigned to a voxel of the region to be imaged is first computed. This distribution function is then convolved with a resolution function, preferably a Gaussian function. Advantageously, the variance of the Gaussian is greater and a multiple of the variance of the noise statistics of the input image. The result of the convolution of the distribution function with the resolution function defines a scale, preferably a grey scale which assigns a particular tone to a pixel of the image corresponding to the voxel of the region to be imaged. The standard deviation is preferably chosen by the user and defines the resolution of the final image in the signal space. The noise in the output image can be decreased by increasing the standard deviation of the convolving Gaussian. For large values of the variance of the Gaussian, the contrast-to-noise ratio is comparable to standard images. The resulting gray scale creates a greater contrast between areas of different volumes having similar signal characteristics. Other resolution functions can be used.

Limit results for sampled systems

Abstract: Properties of discrete time systems obtained by sampling continuous time systems are described. By introducing prefilters, we can treat different ways of sampling within one framework. Results on the convergence of poles and zeros of transfer functions and noise filters as the sampling interval tends to zero are given. These results are generalizations of the results of Astrom, Hagander and Sternby (1984) on the convergence of poles and zeros for zero-order hold sampled transfer functions. Sampled noise models are also analysed. Knowledge of these properties is very important in, for example, discrete time simulations of continuous time systems, and identification of continuous time models based on discrete time measurements.

Time-integrating interferometry using photorefractive fanout.

Abstract: We have experimentally demonstrated a single-beam interferometer that effectively subtracts an exponentially weighted history of the input from the current value, thus functioning as a novelty filter. The single-beam interferometer uses signal depletion due to noise amplification (fanout) in a specially cut crystal of photorefractive BaTiO(3). To demonstrate its real-time operation we used a Hughes liquid-crystal light valve to convert a video image into a phase- and/or amplitude-modulated input signal. Potential applications of this interferometer include image-clutter removal, motion detection and tracking, edge enhancement, and image time differentiation.

Adaptive schemes for noise filtering and edge detection by use of local statistics

Abstract: Some adaptive schemes for noise filterings and edge detection of digital signals are developed. They are bases on the minimum-mean-square-error estimate of the information-bearing signal corrupted by additive noise. The estimate is computed using the local statistics of the input signal and noise. The output is fed back to the input, and the difference between the input and the output is used as the noise estimator. The local statistics of signal and noise are computed through a moving signal window and a moving noise window, which are over the input signal and the noise estimator, respectively. These schemes change their performance according to the local signal-to-noise ratio adaptively. Two kinds of adaptive filtering algorithms and an edge detection algorithm are considered. Their performance in the presence of noise is evaluated and compared to the performance of some other methods. Simulation results on one-dimensional signals and real images are presented. >

Modulation and gap detection for broadband and filtered noise signals

Abstract: Modulation detection thresholds (as a function of sinusoidal amplitude modulation frequency) and temporal gap detection thresholds were measured for three low‐pass‐filtered noise signals (fc =1000, 2000, and 4000 Hz), a high‐pass‐filtered noise signal (fc =4000 Hz), and a broadband signal. The two latter noise signals were effectively low‐pass filtered (fc =6500 Hz) by the earphone. Each of the filtered signals was presented with a complementary filtered noise masker. Modulation and gap detection thresholds were lowest for the broadband and high‐pass signals. Thresholds were significantly higher for the low‐pass signals than for the broadband and high‐pass signals. For these tasks and conditions, the high‐frequency content of the noise signal was more important than was the signal bandwidth. Sensitivity (s) and time constant (τ) indices were derived from functions fitted to the modulation detection data. These indices were compared with gap detection thresholds for corresponding signals. The gap detection...

Active acoustic attenuation system with differential filtering

Abstract: An adaptive active acoustic attenuation system is provided with extended frequency range to attenuate undesired noise which was previously filtered out to avoid instability of the adaptive model. The input signal from the input microphone to the model and the error signal from the error microphone to the model are differentially bandpass filtered to provide a narrower frequency range error signal. In one embodiment, the model operates in its stable region to provide accurate well behaved correction signals to the cancelling loudspeaker, while still receiving a low frequency input noise signal from the input microphone including frequencies below such range. Minimum attenuation frequency has been reduced by at least an octave.

Arrangement for cancelling intersymbol interference and noise

Abstract: An arrangement for combatting intersymbol interference and noise introduced into a data signal transmitted at a symbol rate 1/T by a transmission channel having a memory span LT corresponding to a number of L consecutive data symbols, comprises a receive filter (RF), a first decision circuit (ID) for forming preliminary symbol decisions in response to the transmitted data signal, a second decision circuit (FD) for forming final symbol decisions, means (FFS and FBS) for compensating pre- and post-cursive inter­symbol interference, and a combining circuit (AD) for for­ming the input signal for the second decision circuit (FD). By selecting in these compensating means the memory span MT of the feedforward section (FFS), and possibly also the memory span NT of the feedback section (FBS), to be smaller than the memory span LT of the transmission channel, a simplification of the implementation of the arrangement is achieved, whereas the attainable transmis­sion quality does not appreciably differ from the transmission quality attainable with MT=NT=LT.

A method of ground-roll elimination

Abstract: Amplitude‐ and frequency‐modulated wave motion constitute the ground‐roll noise in seismic reflection prospecting. Hence, it is possible to eliminate ground roll by applying one‐dimensional, linear frequency‐modulated matched filters. These filters effectively attenuate the ground‐roll energy without damaging the signal wavelet inside or outside the ground roll’s frequency interval. When the frequency bands of seismic reflections and ground roll overlap, the new filters eliminate the ground roll more effectively than conventional frequency and multichannel filters without affecting the vertical resolution of the seismic data.

Eigenimage Processing of Seismic Sections

Abstract: This chapter briefly reviews the important theoretical aspects of eigenimage processing and demonstrates the unique properties of this approach using various examples such as the separation of up and downgoing waves, multiple attenuation, and residual static correction. In particular, we will compare the eigenimage technique to the well-known frequency-wave number f k , method, (Treitel et al., 1967), and discuss important differences which arise especially with respect to spacial aliasing and the separation of signal and noise.

Induction motor control apparatus

Abstract: An induction motor vectro control apparatus calculates a slip factor using a secondary time constant. A reference signal is added as noise to the excitation current command signal. The secondary time constant is brought closer to its actual value by modifying the secondary time constant in a direction zeroing the correlation between the motor speed feedback signal and the reference signal.

Quadratic digital filters for image processing

Abstract: A class of nonlinear two-dimensional filters is presented. A matrix description is developed for such operators, and some symmetry conditions are imposed which simplify and render more effective the filter structure. A strategy for the design of filters capable of enhancing images having reduced contrast and degraded by noise is proposed. >

Distributed noise cancellation system

Abstract: A method and system for cancelling noise from sources that are distributed over a region, whereby two sensors are located so that a first sensor will detect both voice signals and noise signals, and a second sensor will detect only the noise signals. The voice signals picked up at the second sensor are negligible, and the noise signals picked up at both sensors are correlated. The signals output from each sensor are connected to a predetermined number of narrowband filters in order to divide each respective signal into a predetermined number of frequencies, such as 15 for example. Thereafter, both signals are combined to cancel effectively the noise component from the signal output having both voice and noise to leave a voice signal that is substantially noise free.

Harmonic decomposition methods in cumulant domains (array processing)

Abstract: The authors address the bearing estimation problem of sources from array data (snapshots) in the presence of Gaussian color (spatially correlated) noises of unknown autocorrelation matrix They demonstrate that the harmonic decomposition methods (signal and noise subspace) can easily be reformulated using fourth-order cumulant matrices instead of autocorrelations Simulation results are presented and comparisons are made to show that the performance of the fourth-order cumulant-based methods (beamforming, MUSIC) is superior to that of their equivalent autocorrelation-based methods when the additive noise sources are colored Gaussian with unknown correlation matrix >