Showing papers on "Noise (signal processing) published in 1990"

On a simple algorithm to calculate the 'energy' of a signal

Abstract: A simple algorithm is derived that permits on-the-fly calculation of the energy required to generate, in a certain sense, a signal. The results of applying this algorithm to a number of well-known signals are shown. Some of the invariance and noise properties of the algorithm are derived and verified by simulation. The implementation of the algorithm and its application to speech processing are briefly discussed. >

Computation of component image velocity from local phase information

Abstract: We present a technique for the computation of 2D component velocity from image sequences. Initially, the image sequence is represented by a family of spatiotemporal velocity-tuned linear filters. Component velocity, computed from spatiotemporal responses of identically tuned filters, is expressed in terms of the local first-order behavior of surfaces of constant phase. Justification for this definition is discussed from the perspectives of both 2D image translation and deviations from translation that are typical in perspective projections of 3D scenes. The resulting technique is predominantly linear, efficient, and suitable for parallel processing. Moreover, it is local in space-time, robust with respect to noise, and permits multiple estimates within a single neighborhood. Promising quantiative results are reported from experiments with realistic image sequences, including cases with sizeable perspective deformation.

A network model of catecholamine effects: gain, signal-to-noise ratio, and behavior

Abstract: At the level of individual neurons, catecholamine release increases the responsivity of cells to excitatory and inhibitory inputs. A model of catecholamine effects in a network of neural-like elements is presented, which shows that (i) changes in the responsivity of individual elements do not affect their ability to detect a signal and ignore noise but (ii) the same changes in cell responsivity in a network of such elements do improve the signal detection performance of the network as a whole. The second result is used in a computer simulation based on principles of parallel distributed processing to account for the effect of central nervous system stimulants on the signal detection performance of human subjects.

Hidden Markov model decomposition of speech and noise

Abstract: The problem of automatic speech recognition in the presence of interfering signals and noise with statistical characteristics ranging from stationary to fast changing and impulsive is discussed. A technique of signal decomposition using hidden Markov models is described. This is a generalization of conventional hidden Markov modeling that provides an optimal method of decomposing simultaneous processes. The technique exploits the ability of hidden Markov models to model dynamically varying signals in order to accommodate concurrent processes, including interfering signals as complex as speech. This form of signal decomposition has wide implications for signal separation in general and improved speech modeling in particular. The application of decomposition to the problem of recognition of speech contaminated with noise is emphasized. >

Objective assessment of image quality: Effects of quantum noise and object variability

Abstract: A number of task-specific approaches to the assessment of image quality are treated. Both estimation and classification tasks are considered, but only linear estimators or classifiers are permitted. Performance on these tasks is limited by both quantum noise and object variability, and the effects of postprocessing or image-reconstruction algorithms are explicitly included. The results are expressed as signal-to-noise ratios (SNR's). The interrelationships among these SNR's are considered, and an SNR for a classification task is expressed as the SNR for a related estimation task times four factors. These factors show the effects of signal size and contrast, conspicuity of the signal, bias in the estimation task, and noise correlation. Ways of choosing and calculating appropriate SNR's for system evaluation and optimization are also discussed.

Spectral self-coherence restoral: a new approach to blind adaptive signal extraction using antenna arrays

Abstract: A new approach to blind adaptive signal extraction using narrowband antenna arrays is presented. The approach has the capability to extract communication signals from cochannel interference environments using only known spectral correlation properties of those signals, i.e. without using knowledge of the content or direction of arrival of the transmitted signal, or the array manifold or background noise covariance of the receiver, to train the antenna array. The class of spectral self-coherence restoral (SCORE) objective functions is introduced, and algorithms for adapting antenna arrays to optimize these objective functions are developed. Using the theory of spectral correlation, it is shown by analysis and simulation that these algorithms maximize the signal-to-interference-and-noise ratio at the output of the narrowband antenna array when a single communication signal with spectral self-coherence at a known value of frequency separation, along with an arbitrary number of interferers without spectral self-coherence at that frequency separation, are impinging on the array. >

Noise reduction system

Abstract: Noise in a speech-plus-noise input signal is suppressed by splitting the input signal into spectral channels and decreasing the gain in the each channel which has a low signal-to-noise ratio (SNR). A voice operated switch (VOX) acts to detect noise-only input to gate a background noise (input signal) estimator and also to gate a residual noise (output signal) estimator. The gain in each of the channels is controlled by the current value (a posteriori) input signal SNR estimate, modified by the prior value (a priori) input signal SNR estimate, and smoothed as a function of the residual (output noise signal) estimate.

Characterization of single channel currents using digital signal processing techniques based on Hidden Markov Models.

Abstract: Techniques for extracting small, single channel ion currents from background noise are described and tested. It is assumed that single channel currents are generated by a first-order, finite-state, discrete-time, Markov process to which is added `white' background noise from the recording apparatus (electrode, amplifiers, etc.). Given the observations and the statistics of the background noise, the techniques described here yield a posteriori estimates of the most likely signal statistics, including the Markov model state transition probabilities, duration (open- and closed-time) probabilities, histograms, signal levels, and the most likely state sequence. Using variations of several algorithms previously developed for solving digital estimation problems, we have demonstrated that: (1) artificial, small, first-order, finite-state, Markov model signals embedded in simulated noise can be extracted with a high degree of accuracy, (2) processing can detect signals that do not conform to a first-order Markov model but the method is less accurate when the background noise is not white, and (3) the techniques can be used to extract from the baseline noise single channel currents in neuronal membranes. Some studies have been included to test the validity of assuming a first-order Markov model for biological signals. This method can be used to obtain directly from digitized data, channel characteristics such as amplitude distributions, transition matrices and open- and closed-time durations.

Automatic image stabilizing system by full-digital signal processing

Abstract: An automatic image-stabilizing system for camcorders and VCRs utilizing only digital signal processing has been developed New technologies for this system are (1) the BERP (band extract representative point) matching technique with a small-scale circuit, (2) an adaptive system control algorithm to discriminate moving objects, and (3) suppression of motion vectors due to noise Calculations show that the motion vector detector requires only 11000 gates, the electronic zoom controller requires only 8500 gates, and only one 8-b field memory is required >

VLSI median filters

Abstract: Median filters have been proposed for the analysis of speech data and in image processing to enhance the data by smoothing the signal and removing noise. Many designs for median filters have been suggested in the literature. The author reviews these designs (most originally given as software filters) and compare them on the basis of suitability for implementation in VLSI. Each design is analyzed in terms of area, time delay, and concurrency. Some new designs are given. The 1-D and 2-D cases are discussed, and recursive median filters are also analyzed. >

Pattern recognition for earthquake detection

Abstract: The detector algorithms in use at date rely on negative decision logic: based on a model of the ambient noise process they detect all deviations, but many of them are false alarms. The principal alternative to this approach is pattern recognition, which tests on positive correlation with some known signal patterns. The Sonogram-detector realizes this scheme for single seismogram traces. Sonograms display spectral energy versus time. Suitably scaled, these images display only information which is significant to the detection process. Patterns of known earthquakes and noise signals are defined by means of these images. Event detection is performed by recognizing one of the patterns in the actual sonogram. The overall processing scheme is similar to the visual inspection of seismograms by the human observer. An off-line test installation for detecting local earthquakes proves the expected low false alarm rate, high timing accuracy and good detection probability of the Sonogram-detector.

Gradient-based adaptive IIR notch filtering for frequency estimation

Abstract: The use of gradient-based algorithms with infinite impulse response (IIR) notch filtering for estimating sinusoids imbedded in noise is investigated. Two notch filter model structures are presented. The first is for applications where two signal sources with correlated noise components can be assessed. The second can be used in situations where only one composite signal source is available. Error surface analysis indicates that second-order modules are unimodal and result in guaranteed convergence. Higher-order modules are multimodal and require judicious choice of initial parameter estimates. Simulation results are included to demonstrate the performance characteristics of both filter structures. >

A methodology for detection and classification of some underwater acoustic signals using time-frequency analysis techniques

Abstract: Signal detection techniques based on time-frequency signal analysis with the Wigner-Ville distribution (WVD) and the cross Wigner-Ville distribution (XWVD) are presented. These techniques are shown to provide high-resolution signal characterization in a time-frequency space, and good noise rejection performance. This type of detection is applied to the signaturing, detection, and classification of specific machine sounds: the individual cylinder firings of a marine engine. For this task, a four-step procedure has been devised. The autocorrelation function (ACF) is first used to ascertain the number of engine cylinders and the firing rate of the engine. Further correlation techniques are then used to detect the time at which individual cylinder firing events occur. WVD- and XWVD-based analyses follow to procedure high-resolution time-frequency signatures. Finally, two-dimensional correlations are used for the classification of the individual cylinders. The proposed methodology is tested on real data. XWVD-based detection is also applied to detection of a transient with unknown waveshape (using real data). >

A methodology for detection and classification of some underwater acoustic signals using time-frequency analysis techniques

Abstract: Signal detection techniques based on time-frequency signal analysis with the Wigner-Ville distribution (WVD) and the cross Wigner-Ville distribution (XWVD) are presented. These techniques are shown to provide high-resolution signal characterization in a time-frequency space, and good noise rejection performance. This type of detection is applied to the signaturing, detection, and classification of specific machine sounds: the individual cylinder firings of a marine engine. For this task, a four-step procedure has been devised. The autocorrelation function (ACF) is first used to ascertain the number of engine cylinders and the firing rate of the engine. Further correlation techniques are then used to detect the time at which individual cylinder firing events occur. WVD- and XWVD-based analyses follow to procedure high-resolution time-frequency signatures. Finally, two-dimensional correlations are used for the classification of the individual cylinders. The proposed methodology is tested on real data. XWVD-based detection is also applied to detection of a transient with unknown waveshape (using real data)

Hearing aid having compensation for acoustic feedback

Abstract: A hearing aid includes a filter in an electrical feedback path, the characteristics of which filter are calculated to model acoustic coupling between the receiver and microphone of the aid. A limiter is inserted in the main electrical pathway between the microphone and the receiver to provide stability in the presence of sudden sound bursts. A noise signal is injected continuously into the electrical circuit and is used to adapt the characteristics of the filter to accommodate changes in the acoustic coupling. The level of the noise signal can be varied to match changes in residual signal level to maintain signal to noise ratio and to optimize rate of adaption commensurate with satisfactory hearing function while the noise itself is unobtrusive to the user.

Generalized gradient schemes for the measurement of two-dimensional image motion

Abstract: This paper describes a procedure for recovering the global velocity of an image by incorporating spatial filtering, and, optionally, temporal filtering, into a scheme that employs a generalized version of the gradient algorithm of motion detection. Motion within a patch is analysed by six parallel channels, each incorporating a different spatiotemporal filter. Advantageous features of this scheme are: (a) global velocity is derived directly, without first computing local velocity at a number of image locations; (b) the filters compute first derivatives rather than second derivatives, making the scheme potentially more resistant to noise than certain other schemes; (c) two of the six filters can be chosen almost completely arbitrarily, and can therefore be tailored to maximize signal reliability, and (d) the measurement of velocity can be made as local or as global as desired by altering the size of the patch that is viewed by the filters. An analogous scheme is derived for the measurement of rotation, as well as expansion or contraction of the image.

Automated Tracking with Target Amplitude Information

Abstract: In this paper we present a data association technique that utilizes the strength of target returns to improve tracking in a cluttered environment. The approach generalizes the Probabilistic Data Association Filter (PDAF) to include the target amplitude, a feature which is available from the detection system that provides measurements for tracking. The probabilistic modelling of target and clutter intensities is based upon collected real data. The corresponding generalized probabilistic data association is derived and improved tracking performance is demonstrated for targets with several signal to noise ratio values.

Direction-of-arrival estimation for wide-band signals using the ESPRIT algorithm

Abstract: A novel direction-of-arrival estimation algorithm is proposed that applies to wideband emitter signals. A sensor array with a translation invariance structure is assumed, and an extension of the ESPRIT algorithm for narrowband emitter signals is obtained. The emitter signals are modeled as the stationary output of a finite-dimensional linear system driven by white noise. The array response to a unit impulse from a given direction is represented as the impulse response of a linear system. The measured data from the sensor array can then be seen as the output of a multidimensional linear system driven by white noise sources and corrupted by additive noise. The emitter signals and the array output are characterized by the modes of the linear system. The ESPRIT algorithm is applied at the poles of the system, the power of the signals sharing the pole is captured, and the effect of noise is reduced. The algorithm requires no knowledge, storage, or search of the array manifold, as opposed to wideband extensions of the MUSIC algorithm. This results in a computationally efficient algorithm that is insensitive to array perturbations. Simulations are presented comparing the wideband and ESPRIT algorithm to the modal signal subspace method and the coherent signal subspace method. >

Telemetry gain adjustment algorithm and signal strength indication in a noisy environment

Abstract: A software-controlled, external programmer for transcutaneously programming and receiving data from an implanted medical device providing a real-time indication of the implanted medical device RF telemetry signal strength to the user of the device programmer while automatically optimizing gain level to minimize interference in the presence of noise. The method and apparatus involves monitoring the validity of received intervals or frames uplinked from the implanted medical device to adjust the gain of the RF amplifier in a pre-determined range. When noise is detected, indicating a relatively small signal-to-noise ratio, the automatic gain control (AGC) level is decreased, effectively tracking this condition. Similarly, lack of any signal or loss of individual RF pulses causes the AGC level to be increased. The signal strength algorithm utilizes this real-time monitoring of signal integrity and signal-to-noise ratio to provide an indication to the user. In addition to the use of current gain level, a secondary factor is included to provide stability of link factor to the signal strength indication to decrease it in the event that a significant quantity of momentary link instabilities are detected. The telemetry gain adjustment algorithm begins operation at minimum signal level, increasing gain value every 45 ms. until a maximum gain level is reached. When at maximum, the gain is reset to minimum, restarting the search. When valid telemetry signals are received, the gain is stabilized for the duration of telemetry transmission.

Detection and suppression of impulsive noise in speech communication systems

Abstract: In the paper a method is presented for the removal of impulsive disturbances from noisy speech and musical signals. The algorithm is based on a detection-interpolation scheme. The design of the impulsive noise detector subsystem is motivated by the observation that linear prediction systems are quite adequate for the modelling of speech signals whereas they can not model impulsive disturbances. It is shown that transforming the speech signal to the glottal excitation may result in significant reduction of the scale of the speech signal to almost that of the excitation signal whereas the scale of impulsive noise does not decrease. This can lead to a significant improvement in the detectability of the noise pulses. Further improvement may be obtained by the application of a matched filter to the noisy excitation signal. The signal samples that are obliterated by impulsive noise are discarded and interpolated. The interpolation method produces a least squared error estimate of the missing samples using speech samples in the vicinity of the missing block and an estimate of the LPC model of the signal. It is shown that the performance of the interpolation system improves significantly when the LPC model incorporates long term correlation structure in addition to the usual short term correlation parameters.

Detecting a transient signal by bispectral analysis

Abstract: A method for detecting a transient waveform of unknown shape in additive stationary noise is presented. The method uses a statistic computed from the sample bispectrum of a sampled record of the signal-plus-noise of length T=N tau , where 1/ tau is the sampling rate. The key result underlying the method is that the bispectrum of the noise is zero in a triangle that is a proper subset of the principal domain triangle. It is shown that the probability of detecting the signal is high if (3/16)/sup 1/2/N/sup 5/6/ is larger than rho /sup -1/, where rho is the energy signal-to-noise ratio. The result implies that the bispectrum-based test may detect a weak signal of unknown form which evades detection by other methods. >

Minimum mean absolute error stack filtering with structural constraint and goals

Abstract: A theory for the structural behavior of stack filters is developed. This theory provides a test which can determine if a given stack filter has any root signals; a method for classifying the root signal behavior of any stack filter found to have roots; and, perhaps most important, a method for designing stack filters with specific root signals or other structural behavior. This theory of root signals for stack filters is then combined with the theory of minimum mean absolute error stack filtering. This unified theory allows the designer to pick a filter which minimizes noise subject to constraints on its structural behavior. >

Method and apparatus for processing quasi-transient telemetry signals in noisy environments

Abstract: A software-controlled, external programmer for transcutaneously programming and receiving data from an implanted medical device providing enhanced discrimination and detection of pulse-interval-coded signals of interest telemetered out of the implanted medical device from undesirable transient and steady-state noise. The programmer incorporates a detector including an active mixer and a precision tuned active phase shifting network providing low level signal rectification, precise narrow bandpass filtering, and 30 decibels of amplification. At the detector, the received signal is mixed with a phase shifted version of itself to produce a detected DC component which is a function of frequency. The DC response emulates a system with a narrow 25 kHz bandpass filter operating at 175 kHz, but does not share its undesirable transient response. For signals in the reject band, the output produces a signal of the opposite polarity of the signals within the pass bands. Transient0 noise excites the receiver antenna and produces a ringing response accompanied by components above 400 kHz. The noise response of the antenna stimulates the detector to produce the intended inverted output. As the transient noise amplitude increases, the inverted response increases in amplitude driving the output level further away from the trigger level of a post-detection comparator. Similarly, any steady state noise signal in the reject band will also result in a steady state inverted response that does not trigger the comparator.

An electromagnetic approach for modeling high performance computer packages

Abstract: Described here is an electromagnetic approach for the analysis of high-performance computer packages such as the thermal conduction module (TCM) used in the IBM 3080 and 3090 processor units. Modeling of signal paths and limitations of previous methods are discussed. Numerical results are presented for propagation characteristics associated with signal lines and vias, and for coupled noise between signal lines. The results are compared with those obtained by means of test vehicles, scale models, and capacitance calculations.

Apparatus and method for generating echographic images

Abstract: An apparatus and method to generate low power ultrasonic, echograph images of selected stationary and moving target objects having high resolution. The apparatus and method include: an apparatus for transmitting a plurality of ultrasonic signals into a selected area of tissue, and apparatus for receiving the corresponding ultrasonic echo signals for each of the transmitted signals. A correlator autocorrelates and cross-correlates the transmitted and received ultrasonic signals. The correlated signals are summed, combined in ratios and partitioned into visibility amplitude data, visibility phase data, differential phase data, closure amplitude data and closure phase data for mapping. The preferred apparatus and method thereafter perform a non-linear image processing, either by an iterative side lobe subtraction signal processing procedure to remove signal noise and/or by an interative hybrid mapping signal processing procedure. The resulting data map yields a high resolution image of the selected target with more data and less noise. Signal processing to show motion or target object changes after noise reduction is also disclosed. In a preferred embodiment, the apparatus and method is employed as a clinical diagnostic tool for generating non-traumatic, high resolution imaging of bodily tissue.

The performance of time-dependent adaptive filters for interference rejection

Abstract: Time-dependent adaptive filters (TDAFs) that allow for the cyclostationary nature of communication signals by periodically changing the filter and adaptation parameters are examined. The TDAF has an advantage over the conventional time-independent adaptive filter in achieving better performance, i.e. reduced mean square error (MSE), for signals with periodic statistics. The basic theory of the TDAF is presented. The TDAF is shown to be more effective than the time-independent adaptive filter for interference rejection. This is verified by theoretical analysis and computer simulation of specific cases of extracting a signal in noise or interference. The criteria for judging the performance of the TDAF for interference rejection are MSE, bit error rate measurements, and constellation diagrams. >

Noise reducing device

Abstract: This invention is concerned with a device for reducing the external noise reaching the ear in extremely noisy places such as in the vehicle or construction sites. According to the present invention, the external noise in picked up by a microphone provided in the vicinity of an electro-acoustic transducer element, such as a headphone unit, provided in the vicinity of the wearer's ear, and the noise signal converted in this manner into electrical signals is produced as the acoustic signal by the electro-acoustic transducer element. The transfer characteristics and controlled in such a manner that the produced noise signal prove to be an acoustic signal which is of the same frequency spectrum and opposite in phase with respect to the external noise reaching the wearer's acoustic meatus from outside to reduce the external noise reaching the acoustic meatus.