Showing papers on "Noise (signal processing) published in 1994"

Active control of noise and vibration

Abstract: An active system for controlling vibration or sound. The system includes a method for limiting the output gain G from a gradient descent algorithm, such as an LMS algorithm, according to the relationship ##EQU1## and reducing one of first and second filter weights W1 and W0 such that G≦Gmax ensuring the output canceling signal is never saturated. This eliminates the square wave shape which may impart unwanted harmonics or resonances to the dynamic system. In another aspect, the system eliminates drift of the output devices toward saturation when situations are encountered such as singularities in the quadratic performance surface. This is accomplished by applying leakage factor r(k) to a function f(k) at or near a predetermined limit to avoid saturation of the output device. The function f(k) can be the gain G or the weights W(k). Another aspect is to provide a constant, optimum, and stable adaptation rate in an LMS system by providing an optimum adaptation coefficient μopt that provides a constant, uniform, and optimum adaptation rate on a per period basis for all frequencies. Various combinations of the above are described and are useful in active control systems.

Signal processing applications of oblique projection operators

Abstract: Oblique projection operators are used to project measurements onto a low-rank subspace along a direction that is oblique to the subspace. They may be used to enhance signals while nulling interferences. In the paper, the authors give several basic results for oblique projections, including formulas for constructing oblique projections with desired range and null space. They analyze the algebra and geometry of oblique projections in order to understand their properties. They then show how oblique projections can be used to separate signals from structured noise (such as impulse noise), damped or undamped interfering sinusoids (such as power line interference), and narrow-band noise. In some of the problems addressed, the oblique projection provides an alternative way to implement an already known solution. Expressing these solutions as oblique projections brings geometrical insight to the study of the solution. The geometry of oblique projections enables one to compute performance in terms of angles between signal and noise subspaces. As a special case of removing impulse noise, the authors can use oblique projections to interpolate missing data samples. In array processing, oblique projections can be used to simultaneously steer beams and nulls. In communications, oblique projections can be used to remove intersymbol interference. >

Adaptive time-frequency decompositions

Abstract: Computing the optimal expansion of a signal in a redundant dictionary of waveforms is an NP-hard problem. We introduce a greedy algorithm, called a matching pursuit, which computes a suboptimal expansion. The dictionary waveforms that best match a signal's structures are chosen iteratively. An orthogonalized version of the matching pursuit is also developed. Matching pursuits are general procedures for computing adaptive signal representations. With a dictionary of Gabor functions, a matching pursuit defines an adaptive time-frequency transform. Matching pursuits are chaotic maps whose attractors define a generic noise with respect to the dictionary. We derive an algorithm that isolates the coherent structures of a signal and describe an application to pattern extraction from noisy signals.

Oscillation and noise determine signal transduction in shark multimodal sensory cells

Abstract: OSCILLATING membrane potentials that generate rhythmic impulse patterns are considered to be of particular significance for neuronal information processing1–4. In contrast, noise is usually seen as a disturbance which limits the accuracy of information transfer5–8. We show here, however, that noise in combination with intrinsic oscillations can provide neurons with particular encoding properties, a discovery we made when recording from single electrosensory afferent of a fish. The temporal sequence of the impulse trains indicates oscillations that operate near the spike-triggering threshold. The oscillation frequency determines the basic rhythm of impulse generation, but whether or not an impulse is actually triggered essentially depends on superimposed noise. The probability of impulse generation can be altered considerably by minor modifications of oscillation baseline and amplitude, which may underlie the exquisite sensitivity of these receptors to thermal and electrical stimuli. Additionally, thermal, but not electrical, stimuli alter the oscillation frequency, allowing dual sensory messages to be conveyed in a single spike train. These findings demonstrate novel properties of sensory transduction which may be relevant for neuronal signalling in general.

Signal processing considerations for liquid ionization calorimeters in a high rate environment

Abstract: We present the results of a study of the effects of thermal and pileup noise in liquid ionization calorimeters operating in a high luminosity environment. The method of optimal filtering of multiply-sampled signals to obtain timing and amplitude from calorimeter signals is described. This method has some advantages over the traditional method of sampling the peak of a shaped signal, which include a reduced sensitivity to channel-to-channel variations in the pre-filter shaping parameters and good performance over a wide range of operating conditions. Analytic expressions for the variance of amplitude and timing measurements are found through a frequency domain approach. Implications for the choice of pre-filter shaping time, number and position of the samples, and digitization accuracy are discussed.

Background Noise Compensation in a Telephone Network

Abstract: An automated method for modifying a speech signal in a telephone network by applying a gain factor which is a function of the level of background noise at a given destination, and transmitting the modified speech signal to the destination. The gain applied may be a function of both the background noise level and the original speech signal. Either a linear or a non-linear (e.g., compressed) amplification of the original speech signal may be performed, where a compressed amplification results in the higher level portions of the speech signal being amplified by a smaller gain factor than lower level portions. The speech signal may be separated into a plurality of subbands, each resultant subband signal being individually modified in accordance with the present invention. In this case, each subband speech signal is amplified by a gain factor based on a corresponding subband noise signal, generated by separating the background noise signal into a corresponding plurality of subbands. The individual modified subband signals may then be combined to form the resultant modified speech signal.

Fundamental frequency estimation of musical signals using a two‐way mismatch procedure

Abstract: Fundamental frequency (F0) estimation for quasiharmonic signals is an important task in music signal processing. Many previously developed techniques have suffered from unsatisfactory performance due to ambiguous spectra, noise perturbations, wide frequency range, vibrato, and other common artifacts encountered in musical signals. In this paper a new two‐way mismatch (TWM) procedure for estimating F0 is described which may lead to improved results in this area. This computer‐based method uses the quasiharmonic assumption to guide a search for F0 based on the short‐time spectra of an input signal. The estimated F0 is chosen to minimize discrepancies between measured partial frequencies and harmonic frequencies generated by trial values of F0. For each trial F0, mismatches between the harmonics generated and the measured partial frequencies are averaged over a fixed subset of the available partials. A weighting scheme is used to reduce the susceptibility of the procedure to the presence of noise or absence ...

A cyclostationary feature detector

Abstract: Cyclostationary models for communications signals have been shown in recent years to offer many advantages over stationary models. Stationary models are adequate in many situations, but they cause important features of the signal to be overlooked. One such important feature is the correlation between spectral components that many signals exhibit. Cyclostationary models allow this spectral correlation to be exploited. This paper presents a signal detector that exploits spectral correlation to determine the presence or absence of a cyclostationary signal in noise. The detector's probability of false alarm is analytically derived. Computer simulations verify that the analytical derivation is correct. The detector's receiver operating characteristic curves are determined from the simulation data and the analytical expression for the probability of false alarm. >

An adaptive estimation of periodic signals using a Fourier linear combiner

Abstract: Presents an adaptive algorithm for estimating from noisy observations, periodic signals of known period subject to transient disturbances. The estimator is based on the LMS algorithm and works by tracking the Fourier coefficients of the data. The estimator is analyzed for convergence, noise misadjustment and lag misadjustment for signals with both time invariant and time variant parameters. The analysis is greatly facilitated by a change of variable that results in a time invariant difference equation. At sufficiently small values of the LMS step size, the system is shown to exhibit decoupling with each Fourier component converging independently and uniformly. Detection of rapid transients in data with low signal to noise ratio can be improved by using larger step sizes for more prominent components of the estimated signal. An application of the Fourier estimator to estimation of brain evoked responses is included. >

Method and apparatus for detecting arcing in alternating current power systems by monitoring high-frequency noise

Abstract: An arc detector or detecting potentially hazardous arcing in electrical connections comprises detection and signal processing circuitry for monitoring high-frequency noise on the power line characteristic of arcing and distinguishable from other sources of high-frequency noise. If high-frequency noise is present and a gap is detected at intervals synchronous to the power frequency, arcing is determined to be present, and an alarm is given.

Speckle noise filtering in ultrasound imaging

Abstract: The enhancement of ultrasound images is provided through the filtering of signal dependent noise such as speckle noise by dividing the signal into selective subintervals and utilizing discrete wavelet transform and the identification and selection of those wavelet transform coefficients primarily including signal and not those primarily including signal dependent noise.

Least mean p-power error criterion for adaptive FIR filter

Abstract: An adaptive FIR filter based on the least mean p-power error (MPE) criterion is investigated. First, some useful properties of MPE function are studied. Three main results are as follows: 1) MPE function is a convex function of filter coefficients; so it has no local minima. 2) When input process and desired process are both Gaussian processes, then MPE function has the same optimum solution as the conventional Wiener solution for any p. 3) When input process and desired process are non-Gaussian processes, then MPE function may have better optimum solution than Wiener solution. Next, a least mean p-power (LMP) error adaptive algorithm is derived and some application examples are presented. Consequently, when the signal is corrupted by an impulsive noise, the adaptive algorithm with p=1 is preferred. Furthermore, when the signal is corrupted by noise or interference, the adaptive algorithm with proper choice of p may be preferred. >

Method of verifying capture of the heart by a pacemaker

Abstract: A method of verifying cardiac capture. A cardiac signal evoked in response to a cardiac stimulation pulse is sensed via an electrode. The sensed signal is lowpass filtered to remove noise and to pass frequencies characteristic of the evoked cardiac signal. The filtered signal is processed to render a waveform signal representing the second derivative of said filtered signal and the second derivative signal is further analyzed to detect a minimum and a maximum amplitude excursion during a selected window of time beginning at a selected time delay following delivery of the cardiac stimulation pulse. The amplitude difference between the minimum and the maximum is measured and compared to a first reference value. The amplitude of the second derivative is measured during a second selected window of time beginning at a selected time delay following delivery of cardiac stimulation pulse, and compared to a second reference value. A capture detect signal is generated if the amplitute difference exceeds the first reference value, but the amplitude does not exceed the second reference value.

Iterative and sequential algorithms for multisensor signal enhancement

Abstract: In problems of enhancing a desired signal in the presence of noise, multiple sensor measurements will typically have components from both the signal and the noise sources. When the systems that couple the signal and the noise to the sensors are unknown, the problem becomes one of joint signal estimation and system identification. The authors specifically consider the two-sensor signal enhancement problem in which the desired signal is modeled as a Gaussian autoregressive (AR) process, the noise is modeled as a white Gaussian process, and the coupling systems are modeled as linear time-invariant finite impulse response (FIR) filters. The main approach consists of modeling the observed signals as outputs of a stochastic dynamic linear system, and the authors apply the estimate-maximize (EM) algorithm for jointly estimating the desired signal, the coupling systems, and the unknown signal and noise spectral parameters. The resulting algorithm can be viewed as the time-domain version of the frequency-domain approach of Feder et al. (1989), where instead of the noncausal frequency-domain Wiener filter, the Kalman smoother is used. This approach leads naturally to a sequential/adaptive algorithm by replacing the Kalman smoother with the Kalman filter, and in place of successive iterations on each data block, the algorithm proceeds sequentially through the data with exponential weighting applied to allow adaption to nonstationary changes in the structure of the data. A computationally efficient implementation of the algorithm is developed. An expression for the log-likelihood gradient based on the Kalman smoother/filter output is also developed and used to incorporate efficient gradient-based algorithms in the estimation process. >

Blind fractionally-spaced equalization, perfect-reconstruction filter banks and multichannel linear prediction

Abstract: Equalization for digitd conimunicatiou~ constitutes a very particular blind deconvoliition problem in that the received signal is cyclostationary. Oversampling (OS) (w.r.t. the symbol rate) of the cyclostationary received signal leads to a stationary vect.or-valued signal (polyphase representation (PR)). OS also leads to a fractionally-spa.ced channel model and equalizer. In the F'R, channel and equalizer can be considered as an analysis and synthesis filter hank. Zero-forcing p equalization corresponds to a perfect-reconstruction Iter bank. We show that in the OS (:aye FIR ZF equalizers exist for a FIR channel. In the PR, the multichannel linear prediction of the noiseless received signal becomes singular eventually, reminiscenc of the singlechannel prediction of a sum of sinusoids. A:. a result, the chaiinel can be identified from the received signal second-order statistics by linear prediction in the noise-free case, and by rising the Pisarenko method when there is a,dditive noise. In the given data case, Music (subspace) or MI. te(hniqiies can be applied.

Vibration/noise control system

Abstract: A sine wave signal generated in synchronism with a pulse signal determining a frequency of vibrations and noises generated by a vibration/noise source is input to a W filter and a C filter. The C filter selects filter coefficients dependent on the rotational speed of an engine, and generates a transfer characteristic-dependent reference signal R dependent on a transfer characteristic of a vibration/noise-transmitting path, based on the filter coefficients. Alternatively, a divisional signal is prepared by dividing a repetition period of vibrations and noises by a predetermined number, and values of a sine wave generated in synchronism with occurrence of said divisional signal is delivered to a W filter, while the transfer characteristic-dependent reference signal is delivered from the C filter storing data of the transfer characteristic identified in advance to the W filter. Alternatively, a sine wave signal and a delayed sine wave signal delayed by a quarter of a repetition period of the sine wave relative to the sine wave, as well as phase and amplitude-related information of the transfer characteristic of the path are generated and delivered in synchronism with generation of the divisional signal. These sine wave signals and the transfer characteristic-dependent reference signal (phase and amplitude-related information) are used to actively control the vibrations and noises.

An SNR estimation algorithm using fourth-order moments

Abstract: An algorithm is presented that allows an estimation of the SNR just by the observation of the noisy signal. For the estimation, shape factors of the signal's and the noise's probability density functions are used. The algorithm is based on the second and fourth order moments of the observed noisy signal. >

Residual noise shaping technique for active noise control systems

Abstract: An active noise control system attenuates the overall sound field. However, in some applications, it is desirable to change the spectral contents of the residual noise. In this Letter, a residual noise spectrum shaping technique based on the filtered‐E least‐mean‐square algorithm has been developed. This technique can be applied to active noise control of one‐dimensional ducts and three‐dimensional enclosures, for both narrow‐band and broadband noises. Computer simulations demonstrate that this method not only attenuates the noise level, but also effectively reshapes the spectrum of the residual noise.

Accuracy analysis of a Hartmann-Shack wavefront sensor operated with a faint object

Abstract: A detailed analysis of the characteristics, regularities, and relationships of the centroiding errors of image spots caused by discrete and limited sampling, photon noise, and readout noise of the detector in a Hartmann-Shack wavefront sensor, wherein an image intensified charge-coupled device used as a photon detector is presented. The theoretical analysis and experimental results herein prove useful for optimum design and application of the sensor.

Non-unit protection technique for EHV transmission systems based on fault-generated noise. Part 1: signal measurement

Abstract: The paper describes the signal processing techniques developed for a new protection technique for EHV transmission lines. The signal processing unit, which forms a major part of the protection scheme, utilises fault-generated high-frequency signals derived from the signal measurement circuit to determine whether the fault is internal or external to the protected zone. The protection scheme has been designed using computer-aided techniques which closely emulate the hardware currently under development and which is being implemented using present-generation hardware.

System for monitoring an industrial process and determining sensor status

Abstract: A method and system for monitoring an industrial process and a sensor. The method and system include generating a first and second signal characteristic of an industrial process variable. One of the signals can be an artificial signal generated by an auto regressive moving average technique. After obtaining two signals associated with one physical variable, a difference function is obtained by determining the arithmetic difference between the two pairs of signals over time. A frequency domain transformation is made of the difference function to obtain Fourier modes describing a composite function. A residual function is obtained by subtracting the composite function from the difference function and the residual function (free of nonwhite noise) is analyzed by a statistical probability ratio test.

Synthetic images by subspace transforms I. Principal components images and related filters

Abstract: The principal component (PC) approach offers compressions of an image sequence into fewer images and noise suppressing filters. Multiple MR images of the same tomographic slice obtained with different acquisition parameters (i.e., with different T R ,T E , and flip angles), time sequences of images in nuclear medicine, and cardiacultrasoundimage sequences are examples of such input image sets. In this paper noise relationships of original and linearly transformed image sequences in general, and specifically of original, PC, and PC‐filtered images are discussed. As the spinoff, it introduces locally weighted PC transforms and filters, nonlinear PC’s, and a single‐image based filter for suppression of noise. Examples illustrate increased perceptibility of anatomical/functional structures in PC images and PC‐filtered images, including extraction of physiological functional information by PC loading curves. Generally, the more correlated the original images are, the more effective is the PC approach.

Analysis of the combined effects of finite samples and model errors on array processing performance

Abstract: The principal sources of estimation error in sensor array signal processing applications are the finite sample effects of additive noise and imprecise models for the antenna array and spatial noise statistics While the effects of these errors have been studied individually, their combined effect has not yet been rigorously analyzed The authors undertake such an analysis for the class of so-called subspace fitting algorithms In addition to deriving first-order asymptotic expressions for the estimation error, they show that an overall optimal weighting exists for a particular array and noise covariance error model In a companion paper, the optimally weighted subspace fitting method is shown to be asymptotically equivalent with the more complicated maximum a posteriori estimator Thus, for the model in question, no other method can yield more accurate estimates for large samples and small model errors Numerical examples and computer simulations are included to illustrate the obtained results and to verify the asymptotic analysis for realistic scenarios >

Detection of non-Gaussian signals using integrated polyspectrum

Abstract: We consider the problem of detecting an unknown, random, stationary, non-Gaussian signal in Gaussian noise of unknown correlation structure. The same framework applies if one desires to determine whether the given random signal is non-Gaussian. The most commonly used method for detection of random signals is the so-called energy detector, which cannot distinguish between Gaussian and non-Gaussian signals and requires the knowledge of the noise power. Recently, the use of bispectrum and/or trispectrum of the signal has been suggested for detection of non-Gaussian signals. The higher order spectra-based detectors do not require the knowledge of the noise statistics if the noise is Gaussian. In this paper, we suggest the use of an integrated polyspectrum (bispectrum of trispectrum) to improve computational efficiency of the detectors based on polyspectrum and to possibly further enhance their detection performance. We investigate conditions under which use of the integrated polyspectrum is appropriate. The detector structure is derived, acid its performance is evaluated via simulations and comparisons with several other existing approaches. >

Noise reduction system for binaural hearing aid

Abstract: In this invention noise in a binaural hearing aid is reduced by analyzing the left and right digital audio signals to produce left and right signal frequency domain vectors and thereafter using digital signal encoding techniques to produce a noise reduction gain vector. The gain vector can then be multiplied against the left and right signal vectors to produce a noise reduced left and right signal vector. The cues used in the digital encoding techniques include directionality, short term amplitude deviation from long term average, and pitch. In addition, a multidimensional gain function based on directionality estimate and amplitude deviation estimate is used that is more effective in noise reduction than simply summing the noise reduction results of directionality alone and amplitude deviations alone. As further features of the invention, the noise reduction is scaled based on pitch-estimates and based on voice detection.

Real-time optical information processing

Abstract: Pattern Recognition and Image Processing: Pattern Recognition with Spatially Disjoint Target and Scene Noise. Optical Pattern Recognition. Improved Invariant Pattern Recognition Methods. Nonlinear Joint Transform Correlators. Image Processing Using Projection Methods. Neural Networks: Optical Associative Processing. Fixed Hologram Neural Networks. Holographic Neural Networks Based on Multi-Grating Processes. Systems, Hardware and Applications: Materials for Spatial Light Modulators. Quantum Noise in Optical Processing. Acoustooptic Correlator for Optical Pattern Recognition. Optical Phase Conjugation for Interconncection and Image Processing. Index.

Subspace methods for the blind identification of multichannel FIR filters

Abstract: A class of methods for identifying a single input/multiple output finite impulse response system (SIMO-FIR), from the outputs of the system only is presented. These methods rely on a minimal parametric representation of the system solution. They are based on the orthogonality between a 'signal' and a 'noise' subspaces. This is exploited to build quadratic forms whose minimization yields the desired estimates up to a scale factor. It is shown (by numerical simulations) that these methods provide significantly better (in terms of bias and variance) estimates than the method by Tong et al. (1991), while requiring about one half the number of computations. They are thus very attractive for applications, in particular, for narrowband TDMA channel equalization. >

Choice of filters for the detection of gravitational waves from coalescing binaries. II. Detection in colored noise.

Abstract: Coalescing systems of compact binary stars are one of the most important sources for the future laser interferometric gravitational wave detectors. The signal from such a source will, in general, be completely swamped out by the photon-counting noise in the interferometer. However, since the wave form can be modeled quite accurately, it is possible to filter the signal out of the noise by the well known technique of matched filtering. The filtering procedure involves correlating the detector output with a copy of the expected signal called a matched filter or a template. When the signal parameters are unknown, as in the case of the coalescing binary signal, it is necessary to correlate the output through a number of filters each with a different set of values for the parameters. The ranges in which the values of the parameters lie are determined from astrophysical considerations and the set of filters must together span the entire ranges of the parameters. In this paper, we show how a choice of filters can be made so as not to miss any signal of amplitude larger than a certain minimum value, called the minimal strength. The number of filters and the spacing between filters in the parameter space are obtained for different values of the minimal strength of the signal. We also present an approximate analytical formula which relates the spacing between filters to the minimal strength. We discuss the problem of detection and false dismissal probabilities for a given data output and how a given set of filters determines these probabilities.

Mobile radio set comprising a speech processing arrangement

Abstract: A speech processing arrangement has at least two microphones for supplying microphone signals formed by speech components and noise components to microphone signal branches that are coupled to an adder device used for forming a sum signal. The microphone signals are delayed and weighted by weight factors in the microphone signal branches. The arrangement includes an evaluation circuit that a) receives the microphone signals, b) estimates the noise components, c) estimates the speech components by forming the difference between one of the microphone signals and the estimated noise component for this microphone signal, d) selects one of the microphone signals as a reference signal which contains a reference noise component and a reference speech component, e) forms speech signal ratios by dividing the estimated speech components by the estimated reference speech component, f) forms noise signal ratios by dividing the powers of the estimated noise components by the power of the estimated reference noise component, and g) determines the weight factors by dividing each speech signal ratio by the associated noise signal ratio. The signal-to-noise ratio corresponds to the ratio of the power of the speech component to the power of the noise component of the sum signal. Because the speech signals are correlated and noise signals are uncorrelated, the sum signal available on the output of the adder device has a reduced noise component yielding improved speech audibility. Real-time computation of the weight factors eliminates any annoying delay during a conversation held using the speech processing arrangement.

Noise suppression system and method therefor

Abstract: A noise suppression system (105) and method therefor includes a signal gain calculator (205) for calculating a constant gain value (701) for an input signal (116) according to a first function (703) responsive to a noise energy estimate (213) and a signal to noise ratio (SNR) estimate (215) until the SNR estimate (215) reaches a first predetermined SNR threshold (705), then calculating a variable gain value (707) for the input signal (116) according to a second function (709) responsive to the noise energy estimate (213) and the SNR estimate (215) until the SNR estimate (215) reaches a second predetermined SNR threshold (711) below the first predetermined SNR threshold (705), then calculating the constant gain value (701) responsive to the noise energy estimate (213) and the SNR estimate (215) when the SNR estimate (215) reaches the second predetermined SNR threshold (711). The constant gain value (701) is less than the variable gain value (707) when the SNR estimate (215) is between the first predetermined SNR threshold (705) and the second predetermined SNR threshold (711). The noise suppression system (105) and method therefor advantageously improves noise suppression without truncating the tail ends of speech while minimizing noise flutter.