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Acoustics, Speech and Signal Processing, IEEE Transactions on

Issue 4 • Date August 1985

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Displaying Results 1 - 25 of 51
  • [Front cover and table of contents]

    Page(s): 0
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  • Author's reply

    Page(s): 1013
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  • Comments on "Pole-zero analysis of voiced speech using group delay characteristics"

    Page(s): 1049 - 1051
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    It is shown that the need for an intermediate estimate of the spectral envelope can be avoided in the determination of group delay characteristics from the short-time cepstrum, provided that the analysis parameters are carefully chosen. View full abstract»

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  • Digital image processing

    Page(s): 1063 - 1064
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  • [Back cover]

    Page(s): c4
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    Freely Available from IEEE
  • Optimal estimation of an unknown deterministic signal vector using a time-invariant filter

    Page(s): 1044 - 1047
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    The problem of estimating a deterministic signal vectorundertilde{theta}fromundertilde{x} = undertilde{theta} + undertilde{n}is considered using quadratic loss. It is assumed that the noiseundertilde{n}is weakly stationary, and that the vector size is large. These assumptions along with a time-invariant filter constraint allow the use of Fourier transforms and a filtering approach. It is noted that in the class of time-invariant data-independent filters, given spectral knowledge of the unknown deterministic signal vectorundertilde{theta}, the best performance is achieved by a form similar to the classical Wiener filter form. This provides the motivation for a simple empirical Wiener estimator, wherein the signal spectral information is estimated from the data. This estimator is shown to dominate the MLE at least in the case where the spectral signal-to-noise ratio is uniformlylsim0.65. View full abstract»

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  • IIR digital filters with equiripple stopband transmission

    Page(s): 1047 - 1049
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    Recently, a simple technique of introducing transmission zeros into a low-pass all-pole filter transfer function to make its stop-band transmission equiripple has been described by Hazra [1]. It is shown that this technique represents a special case of a more general procedure which Provides not only Hazra's solution, but also additional filters which are advantageous with regard to the required number of multiplications per sample. The results are demonstrated with the aid of an example. View full abstract»

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  • An efficient and order-recursive algorithm for estimating stationary ARMA models

    Page(s): 1054 - 1058
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    A new Levinson-type recursive algorithm that solves for the coefficients of the pth-order ARMA model using O(p2) operations is proposed. The algorithm uses the fact that in the least squares optimal equation when the system is stationary, the coefficient matrix is "close" to the Toeplitz form. The measures of closeness of a matrix to Toeplitz form is determined by the concept of displacement rank. View full abstract»

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  • On spatial smoothing for direction-of-arrival estimation of coherent signals

    Page(s): 806 - 811
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    We present an analysis of a "spatial smoothing" preprocessing scheme, recently suggested by Evans et al., to circumvent problems encountered in direction-of-arrival estimation of fully correlated signals. Simulation results that illustrate the performance of this scheme in conjunction with the eigenstructure technique are described. View full abstract»

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  • Efficient methods of estimate correlation functions of Gaussian processes and their performance analysis

    Page(s): 1032 - 1035
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    New efficient methods to estimate crosscorrelation functions of Gaussian signals are studied. In these methods, the "covariance property" of the Gaussian distribution is utilized such that the correlation estimates can be computed with only additions. To evaluate the performances of the methods, exact expressions for the bias and variance of these estimators are formulated and utilized in comparing these methods with the conventional correlation estimator. As a result, we point out that these new methods can give estimates which are comparable to the conventional approach. View full abstract»

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  • The iterative NCDE algorithm for ARMA system identification and spectral estimation

    Page(s): 1021 - 1024
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    The problem of identifying autoregressive moving average (ARMA) models with observational output data is addressed within this report. In the absence of actual input data, the ARMA identification problem is nonlinear in the parameters. The new general ARMA algorithm derived within, entitled NCDE, makes use of the Yule-Walker equations for input estimation and a least squares input-output ARMA algorithm for initial parameter estimation. The NCDE algorithm has been tested and results show that it is both effective and efficient for autoregressive (AR), moving average (MA) and ARMA system identification via the application of an ARMA model. View full abstract»

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  • Classification of textures using Gaussian Markov random fields

    Page(s): 959 - 963
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    The problem of texture classification arises in several disciplines such as remote sensing, computer vision, and image analysis. In this paper we present two feature extraction methods for the classification of textures using two-dimensional (2-D) Markov random field (MRF) models. It is assumed that the given M × M texture is generated by a Gaussian MRF model. In the first method, the least square (LS) estimates of model parameters are used as features. In the second method, using the notion of sufficient statistics, it is shown that the sample correlations over a symmetric window including the origin are optimal features for classification. Simple minimum distance classifiers using these two feature sets yield good classification accuracies for a seven class problem. View full abstract»

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  • On the sliding-window representation in digital signal processing

    Page(s): 868 - 873
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    The short-time Fourier transform of a discrete-time signal, which is the Fourier transform of a "windowed" version of the signal, is interpreted as a sliding-window spectrum. This sliding-window spectrum is a function of two variables: a discrete time index, which represents the position of the window, and a continuous frequency variable. It is shown that the signal can be reconstructed from the sampled sliding-window spectrum, i.e., from the values at the points of a certain time-frequency lattice. This sampling lattice is rectangular, and the rectangular cells occupy an area of 2π in the time-frequency domain. It is shown that an elegant way to represent the signal directly in terms of the sample values of the sliding-window spectrum, is in the form of Gabor's signal representation. Therefore, a reciprocal window is introduced, and it is shown how the window and the reciprocal window are related. Gabor's signal representation then expands the signal in terms of properly shifted and modulated versions of the reciprocal window, and the expansion coefficients are just the values of the sampled sliding-window spectrum. View full abstract»

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  • Real discrete Fourier transform

    Page(s): 880 - 882
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    The real discrete Fourier transform (RDFT) corresponds to the Fourier series for sampled periodic signals with sampled periodic frequency responses just as discrete Fourier transform (DFT) corresponds to the complex Fourier series for the same type of signals. RDFT has better performance than DFT in data compression and filtering for all signals in the sense that Pearl's measure for RDFT is less than Pearl's measure for DFT by an amount ΔW. RDFT also has better performance than DFT in the computation of real convolution because of the reduced number of operations, and the fact that forward and inverse transforms can be implemented with the same signal flowgraph, thereby facilitating hardware and software design. View full abstract»

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  • Selecting the order of autoregressive models from small samples

    Page(s): 874 - 879
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    The weak parameter criterion WPC is introduced as a means for model order selection. It is based on the same principles as Mallows' Cpand the FPE and AIC criteria of Akaike. According to the WPC, parameters are weak and should be removed if the squares of their estimates are less than twice the expectation for a white noise signal. Roughly speaking, the square of an estimate must exceed twice its variance. Due to the conceptual simplicity, this criterion remains useful for small samples where the asymptotical properties are no longer valid. If the maximum order considered issqrt{N}or less, the difference between Akaike's FPE and AIC criteria on one hand and the WPC on the other hand remains small and the use of AIC or FPE may be justified. However, it is advised to use the WPC for higher maximum orders. By using different variances in the WPC for Yule-Walker and for Burg estimates, it is achieved that the average selected WPC order in small samples depends mainly on the given data and no longer on the method of parameter estimation. View full abstract»

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  • On suppression of a sinusoidal signal in broad-band noise

    Page(s): 1024 - 1026
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    In an estimator-subtractor configuration of the generalized adaptive line enhancer (ALE), a sinusoidal signal (in broad-band noise) is estimated by the two-sided and one-sided Wiener and linear prediction filters. For a fixed number of input samples, the estimation error in Wiener and prediction filters is shown to be minimum, when both future and past samples, not necessarily equal in numbers, are utilized in the present estimate of the signal. The error in the estimate of the signal by the two-sided Wiener filter is also shown to be equal to that by the optimized form of the well-known ALE. View full abstract»

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  • Analysis of N-D general-support filters

    Page(s): 972 - 982
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    It is shown that every recursible N-D IIR general-support filter has its impulse response support contained in a causality sector. A procedure to find such a causality sector in N-dimensions (N-D) is described. These results lead to simple stability theorems for general-support N-D IIR filters. It is also shown that the modeling of an N-D digital filter using causality sectors suggests a systematic method for obtaining canonical local state space representation which is compatible with its input-output description. View full abstract»

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  • Realization of adaptive digital filters using the Fermat number transform

    Page(s): 1036 - 1039
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    An efficient realization of the block least mean-square adaptive digital filters using fast transforms is discussed. The computational complexities of the Fermat number transform realization and the fixed-point fast Fourier transform realization are compared and their convergence properties are studied through the computer simulation of practical applications. View full abstract»

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  • Waveform estimation using group delay processing

    Page(s): 832 - 836
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    A method of signal waveform estimation from an ensemble of jittered noisy measurements is presented. The method uses group delay functions to perform the ensemble averaging and thus overcomes the difficulty of computing the unwrapped phase function before averaging. We propose a new technique, called group delay processing, to estimate the signal waveform if only a single noisy measurement is available. We demonstrate our group delay averaging and group delay processing techniques through illustrative examples. View full abstract»

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  • The tone capture properties of CMA-based interference suppressors

    Page(s): 946 - 958
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    An earlier paper introduced the constant modulus algorithm (CMA), an adaptive filtering technique for correcting multipath and interference-induced degradations in constant envelope waveforms such as FM and QPSK signals. This algorithm exploits the fact that both multipath propagation and additive interference disrupt the constant envelope property of the received signal. By sensing the received envelope variations, the adaptive algorithm can reset the coefficients of an FIR digital filter so as to remove the variations and, in the process, suppress the various interference components from the desired signal. This paper examines a problem that arises when using CMA to suppress narrow-band interference. If both the interferer and the signal have constant envelope and are spectrally nonoverlapping, then it is possible to find two different filter solutions, one which suppresses the interferer and another which "captures" the interferer and suppresses the desired signal. This paper examines how "capture, can occur and how it may be prevented. This problem is studied by characterizing the algorithm's behavior to an input consisting of only two sinusoids. Assuming slow adaptation, the N-dimensional adaptive weight recursion is shown to compress into a two-by-two recursion in the tone output amplitudes. This simplified recursion is then analyzed to determine what combinations of input amplitudes (signal-to-interference ratios) and filter initial conditions lead to "lock" and which lead to the capture of the interferer. The results are then broadened to include multiple input tones and signals with nonzero bandwidth. View full abstract»

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  • An iterative algorithm for inverse filtering

    Page(s): 1051 - 1054
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    If the Z transform of a filter has some zero points which are on or very close to the unit circle, the inverse filter will extend for many significant terms instead of vanishing in a few terms, and a tremendous truncation error will result. An iterative algorithm has been found to handle this zero-point problem. This paper describes the iterative algorithm and shows mathematically that it converges and that the limit is equal to the original signal. Numerical results indicate that the restoration of the original signal is reasonably good. View full abstract»

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  • A unified approach to the optimal synthesis of fixed-point state-space digital filters

    Page(s): 911 - 920
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    This paper proposes two expressions of the output error variance due to coefficient quantization of fixed-point state-space digital filters in the time domain. One is the deterministic approach, which gives precisely the output error variance of state-space digital filters. The other is the statistical approach, where the errors of coefficient quantization are assumed to be independent random variables. The statistical approach gives a simple and easy way to analyze the output error variance due to coefficient quantization of state-space digital filters. The statistical coefficient sensitivity introduced by the statistical approach is shown to be equivalent to the round off noise power gain. Thus, state-space digital filters which are optimal with respect to both coefficient sensitivity and roundoff noise can be synthesized by the method of minimization of the roundoff noise. Such optimal state-space digital filters which could be of any order are proved to be free of autonomous overflow limit cycles. A numerical example is given to illustrate the effectiveness of the analysis of the output error variance due to coefficient quantization and the synthesis method proposed here. View full abstract»

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Aims & Scope

This Transactions ceased production in 1990. The current retitled publication is IEEE Transactions on Signal Processing.

Full Aims & Scope