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Acoustics, Speech and Signal Processing, IEEE Transactions on

Issue 1 • Date January 1987

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Displaying Results 1 - 19 of 19
  • [Front cover and table of contents]

    Page(s): 0
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    Freely Available from IEEE
  • Editorial transactions goes monthly

    Page(s): 1
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    Freely Available from IEEE
  • [Back cover]

    Page(s): c4
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    Freely Available from IEEE
  • An algorithm for computing general integer-order Hankel transforms

    Page(s): 86 - 97
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    A tomographic image reconstruction algorithm that we have been studying requires the nth-order Hankel transform for the nth function in a series, where n varies over a set of integers. Unfortunately, most previously proposed algorithms have either dealt with only zeroth-order transforms or cannot be applied conveniently to our problem. We propose an algorithm for computing general integer-order Hankel transforms. The algorithm takes samples of equally spaced input functions and gives equally spaced samples of the transforms. View full abstract»

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  • A new method for synthesis of low-peak-factor signals

    Page(s): 120 - 122
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    For a specified power spectrum, the minimum-to-maximum amplitude range of a periodic signal depends on the phase angles of the harmonics. A low value of this range, which as a fraction of the root-mean-square value is usually called the peak factor, is often desirable. A new phase angle adjusting method is proposed producing lower peak factors than the conventional method. View full abstract»

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  • Recursive image registration with application to motion estimation

    Page(s): 70 - 85
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    An algorithm for real-time recursive estimation of the two-dimensional shift between successive images in an image sequence occurring, e.g., in a down-looking airborne TV sensor, is presented. The shift, which is linearly related to ground velocity, is adaptively tracked by means of the gradient of a similarity function relating the two successive images. An implementation is presented in which substantial memory capacity and computational complexity are saved by using only a single line in the image frame and by binary quantization of the video signal. A detailed analysis of the algorithm is presented using stochastic mathematical models for the terrain texture, image noise, and velocity variations. Under some simplifying assumptions, closed-form solutions for the error statistics, including temporal power spectral density, are derived. The probability of loss of lock in tracking and the expected time for its reacquisition are evaluated and are found to be low at typical operating conditions. The analysis also indicates considerable robustness of the algorithm to widely different image statistics. Numerical examples indicate very good performance in autonomous navigation applications. View full abstract»

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  • Tunable digital frequency response equalization filters

    Page(s): 118 - 120
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    Tunable digital frequency response equalization filters, which feature adjustable gain at specified frequencies while leaving the remainder of the spectrum unaffected, are advanced. The filter structure is such that the frequency response parameters are independently related to the multiplier coefficients, which permits simple frequency response adjustment by varying the coefficient values. The resulting structure exhibits low coefficient sensitivity characteristics. View full abstract»

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  • Optimality in the choice of the convergence factor for gradient-based adaptive algorithms

    Page(s): 48 - 59
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    The convergence and the adaptation speed of gradient-based adaptive algorithms are controlled by the chosen value for the convergence factor μ. In this paper, the existence of an optimal value for this convergence factor is investigated for two classes of algorithms. A proof is first presented for the general case of the complex adaptive-linear-combiner (ALC). The results are applied to the complex and real LMS algorithms. This is followed by a second proof for algorithms which are linear only in a subset of their adaptive coefficients. These cases are found in IIR applications such as the hybrid-recursive, lattice-recursive, and recursive algorithms using the direct realization IIR. For each case, the optimal value is shown to be generated using instantaneous signal estimates. The resulting adaptive algorithms become self-optimizing in terms of their convergence factor, and dependence on incoming training signal levels is reduced. Moreover, a correction factor is introduced in each case to regulate the adaptation process and accommodate practical applications where additive signals are present with the desired signal. View full abstract»

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  • Deterministic properties of the recursive separable median filter

    Page(s): 98 - 106
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    The recursive separable median filter has been successfully used to extract features from noisy two-dimensional signals. In many applications, it gives better noise suppression and edge preservation than the standard separable median filter. In this paper we use a new approach for studying the deterministic properties of separable median filters. In particular, using threshold decomposition, we derive the root structure of the recursive separable median filter, where a root is a signal invariant to further filtering. It is shown that these root structures differ from those of their nonrecursive counterparts. We also show that any two-dimensional signal will converge to a root after repeated passes of the recursive separable median filter. View full abstract»

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  • An algorithm to find two-dimensional signals with specified zero crossings

    Page(s): 107 - 111
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    We present an iterative method for finding two-dimensional band-limited functions with predetermined zero crossings. This problem arises when we want to generate a prescribed binary image at the output of a diffraction-limited imaging system with high-contract recording. An important application of this image generation problem is in the design of masks for microlithography. After describing the algorithm, a proof of its convergence, based on the properties of nonexpansive mappings, is given. The relevance of this algorithm to the image construction problem is then discussed, and we conclude by illustrating two patterns generated by our proposed method. View full abstract»

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  • Optimization of voiced/Unvoiced decisions in nonstationary noise environments

    Page(s): 9 - 18
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    This paper describes a way of optimizing the autocorrelation method of voiced/unvoiced decision for speech which is heavily degraded by nonstationary ambient noise. Usually a constant threshold is adopted to which the correlation peak value is compared for voiced/ unvoiced decision. The optimal threshold is, however, a function of noise characteristics and the signal-to-noise ratio. This paper presents a method of estimating the probability density function of correlation peak values from noisy speech and also of estimating the optimal threshold based on the expected error rate of the voiced/unvoiced decision. The performance of the proposed method has been tested under various noise characteristics and signal-to-noise ratios. These tests show that the estimated threshold is very close to the true optimal threshold in almost all cases. The proposed method also retains the optimality under slowly time-varying noise conditions, even if no a priori information is given about noise characteristics or noise level. View full abstract»

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  • On using cooccurrence matrices to detect periodicities

    Page(s): 114 - 116
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    Drawbacks and limitations in applying the χ2independence hypothesis test parameter over the elements of cooccurrence matrices are discussed. The proper kind of measures to be applied, essentially of degree of concentration on the main diagonal, is justified. Some simple parameters that can be used in this direction are introduced, and their performances illustrated using simulations. The possibility of designing "ad hoc" tests is also suggested. View full abstract»

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  • Application of the Karhunen-Loève transform to diffraction separation

    Page(s): 2 - 8
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    The Karhunen-Loève (K-L) transform is applied to the separation of diffractions from reflections, for the model problem of a rigid half-plane. The basic transform and its interpretation in the context of seismic data are briefly reviewed. The transform, computed via the singular value decomposition, is applied to model data generated using an acoustic transceiver, which travels above a metal plate representing the half-plane. The K-L transform yields a clear separation between the edge diffraction and reflection. This is followed by an application of the slant K-L transform to remove dipping wall reflections which interfere with the edge diffraction. View full abstract»

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  • Two-dimensional block transforms and their properties

    Page(s): 112 - 114
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    For two-dimensional (2-D) digital filters implemented by a block recursive equation, explicit relations between their frequency characteristics and those of scalar filter are obtained. Specifically, these include the relation between the discrete-time Fourier transform (DTFT) of the block recursive equation and that of the scalar 2-D difference equation, and the relation between the block matrix transfer function of the block processor and the scalar transfer function. These relations that are independent of the type of realization of the block processor have been obtained using the eigenvalue properties of a special type of circulant matrix introduced in this correspondence. View full abstract»

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  • Theoretical analysis of the max/Median filter

    Page(s): 60 - 69
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    Median filtering has been used successfully for extracting features from noisy one-dimensional signals; however, the extension of the one-dimensional case to higher dimensions has not always yielded satisfactory results. Although noise suppression is obtained, too much signal distortion is introduced and many features of interest are lost. In this paper, we introduce a multidimensional filter based on a combination of one-dimensional median estimates. It is shown that threshold decomposition holds for this class of filters, making the deterministic analysis simpler. Invariant signals to the filter, called root signals, consist of very low resolution features making this filter much more attractive than conventional median filters. View full abstract»

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  • Interpolation and extrapolation of an ideal band-limited random process

    Page(s): 43 - 47
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    This paper considers the problem of interpolation and extrapolation of a band-limited random process from a finite set of N equally spaced samples using a linear mean-square estimation approach. Various theoretical properties of the solutions are discussed, such as the effect of sample spacing, number of samples, and bandwidth. Computer solutions are presented that are useful for both illustrating the theoretical behavior and providing quantitative measures of the interpolation and extrapolation error. It is demonstrated that Nyquist sampling at twice the highest frequency is not an appropriate criterion for finite N: undersampling can give satisfactory performance for a band-pass process, while oversampling can be required for a low-pass process. These conclusions are similar to known results for sampling over infinite intervals. View full abstract»

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  • Simulation of fixed-point operations with high-level languages

    Page(s): 116 - 118
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    A method for simulating fixed-point two's-complement arithmetic operations with high-level programming languages is presented. It is based on the use of positive integers that are in correspondence with the bit combination of the binary number that would appear in the real system. Fortran procedures are given in the Appendix. View full abstract»

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  • Linear modeling and the coherence function

    Page(s): 19 - 28
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    In this paper, a tutorial introduction to the magnitude squared (MS) coherence function, and its relation to linear modeling, is interleaved with new methods for characterizing bidirectional and unidirectional linear association between two time series. A new method for characterizing bidirectional linear association is based on the new concept of an MS coherence sequence which is the inverse z-transform of the MS coherence function. The MS coherence function is approximated as a single real-valued ratio of polynominals in z. The ratio is determined by a matrix equation whose entries are convolutions of correlation functions. Solutions for the corresponding MS coherence parameters employ an eigenvalue-eigenvector decomposition. The algorithm is compared to a traditional periodogram-based method using computer simulations. The computer simulations also demonstrate the effects of parameter overdetermination and overordering. The sensitivity of the matrix equations for the computer simulations is computed via the numerical linear algebra concept of condition number. View full abstract»

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  • Linear statistical models for stationary sequences and related algorithms for Cholesky factorization of Toeplitz matrices

    Page(s): 29 - 42
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    We review the Levinson-Durbin recursions for computing the Cholesky factors of the inverse of a Toeplitz correlation matrix R, and the Schur recursions for computing the Cholesky factors of R itself. We show that both algorithms may be imbedded in a single vector lattice recursion. Then, depending only on how the initial conditions are set, one gets either the Levinson-Durbin or the LeRoux-Gueguen recursions for computing reflection coefficients. We extend the analysis to treat the Toeplitz nonsymmetric case, and place the corresponding algorithms into the same framework. We also review the relation between the vector lattice recursions and their implementations in a serial lattice structure. Then we show how the factorizations may be run backwards to compute the Cholesky factors and correlations from the reflection coefficients. This generalizes a result usually attributed to Robinson and Treitel. One of our main purposes is to emphasize that the Levinson-Durbin recursions for going back-and-forth between correlations, reflection coefficients, and autoregressive filter parameters may be replaced with a dual set of recursions for going back-and-forth between correlations, reflection coefficients, and moving average filter parameters. When the correlation sequence is ARMA (p, p), then we show how the Schur recursions simplify to the Morf-Sidhu-Kailath algorithm for computing Kalman gains. Finally, the MSK algorithm may be specialized to the Rissanen algorithm for factoring a pure MA correlation sequence. 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