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Acoustics, Speech and Signal Processing, IEEE Transactions on

Issue 6 • Date December 1986

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Displaying Results 1 - 25 of 42
  • [Front cover and table of contents]

    Page(s): 0
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    Freely Available from IEEE
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  • [Back cover]

    Page(s): c4
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    Freely Available from IEEE
  • A comparison of numerator estimators for ARMA spectra

    Page(s): 1668 - 1671
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    This correspondence investigates the problems of estimating the numerator spectrum corresponding to an ARMA time series model once the denominator spectrum (i.e., the AR coefficients) has been estimated. A general form for an estimator of the numerator spectral (NS) coefficients is developed first. Six NS estimators from the recent literature are then compared by fitting them into this general framework and extracting their particular characteristics. It is shown that some methods are special cases of other methods, and that several of these methods are asymptotically equivalent. View full abstract»

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  • Zero-tracking adaptive filters

    Page(s): 1566 - 1572
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    A new type of adaptive filter is proposed which can directly estimate and track its own zeros. The adaptation algorithm adapts the zeros of the filter and hence, indirectly, the filter coefficients. To first order in the adaptation parameter, the new algorithm is equivalent to the usual LMS algorithm, and thus it shares the same convergence properties with the latter. The cases of adaptive prediction, the adaptive Pisarenko method, and adaptive point-source location are discussed in detail. View full abstract»

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  • Speech transformations based on a sinusoidal representation

    Page(s): 1449 - 1464
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    In this paper a new speech analysis/synthesis technique is presented which provides the basis for a general class of speech transformations including time-scale modification, frequency scaling, and pitch modification. These modifications can be performed with a time-varying change, permitting continuous adjustment of a speaker's fundamental frequency and rate of articulation. The method is based on a sinusoidal representation of the speech production mechanism which has been shown to produce synthetic speech that preserves the wave-form shape and is perceptually indistinguishable from the original. Although the analysis/synthesis system was originally designed for single-speaker signals, it is also capable of recovering and modifying nonspeech signals such as music, multiple speakers, marine biologic sounds, and speakers in the presence of interferences such as noise and musical backgrounds. View full abstract»

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  • The computation of line spectral frequencies using Chebyshev polynomials

    Page(s): 1419 - 1426
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    Line spectral frequencies provide an alternate parameterization of the analysis and synthesis filters used in linear predictive coding (LPC) of speech. In this paper, a new method of converting between the direct form predictor coefficients and line spectral frequencies is presented. The system polynomial for the analysis filter is converted to two even-order symmetric polynomial with interlacing roots on the unit circle. The line spectral frequencies are given by the positions of the roots of these two auxiliary polynomials. The response of each of these polynomials on the unit circle is expressed as a series expansion in Chebyshev polynomials. The line spectral frequencies are found using an iterative root finding algorithm which searches for real roots of a real function. The algorithm developed is simple in structure and is designed to constrain the maximum number of evaluations of the series expansions. The method is highly accurate and can be used in a form that avoids the storage of trigonometric tables or the computation of trigonometric functions. The reconversion of line spectral frequencies to predictor coefficients uses an efficient algorithm derived by expressing the root factors as an expansion in Chebyshev polynomials. View full abstract»

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  • Waveform substitution techniques for recovering missing speech segments in packet voice communications

    Page(s): 1440 - 1448
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    Packet communication systems cannot, in general, guarantee accurate and prompt delivery of every packet. The effect of network congestion and transmission impairments on data packets is extended delay; in voice communications these problems lead to lost packets. When some speech packets are not available, the simplest response of a receiving terminal is to substitute silence for the missing speech. Here, we explore techniques for replacing missing speech with wave-form segments from correctly received packets in order to increase the maximum tolerable missing packet rate. After presenting a simple formula for predicting the probability of waveform substitution failure as a function of packet duration and packet loss rate, we introduce two techniques for selecting substitution waveforms. One method is based on pattern matching and the other technique explicitly estimates voicing and pitch. Both approaches achieve substantial improvements in speech quality relative to silence substitution. After waveform substitution, a significant component of the perceived distortion is due to discontinuities at packet boundaries. To reduce this distortion, we introduce a simple smoothing procedure. View full abstract»

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  • An optimal technique for constraint-based image restoration and reconstruction

    Page(s): 1629 - 1642
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    A new technique for finding an optimal feasible solution to the general image reconstruction and restoration problem is described. This method allows the use of prior knowledge of the properties of both the solution and any noise present on the data. The problem is formulated as the optimization of a cost function over the intersection of a number of convex constraint sets; each set being defined as containing those solutions consistent with a particular constraint. A duality theorem is then applied to yield a dual problem in which the unknown image is replaced by a model defined in terms of a finite dimensional parameter vector and the kernels of the integral equations relating the data and solution. The dual problem may then be solved for the model parameters using a gradient descent algorithm. This method serves as an alternative to the primal constrained optimization and projection onto convex sets (POCS) algorithms. Problems in which this new approach is appropriate are discussed. An example is given for image reconstruction from noisy projection data; applying the dual method results in a fast nonlinear algorithm. Simulation results demonstrate the superiority of the optimal feasible solution over one obtained using a suboptimal approach. View full abstract»

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  • On averaging burg spectral estimators for segments

    Page(s): 1473 - 1484
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    The Burg spectral estimator (BSE) exhibits better peak resolution than conventional linear spectral estimators, particularly for short data records. Based on this property, the quality of the BSE is investigated with the available data record segmented and the relevant parameters or functions associated with each segment averaged. Averaging of autoregressive coefficients, reflection coefficients, or spectral density functions is used with the BSE, and the corresponding performances are studied. Approximate expressions for the mean and variance of these modified Burg spectral estimators are derived. The variance of the estimation errors associated with the modified power spectral density estimators is compared to the theoretical Cramer-Rao lower bound. It is observed from the results that averaging of reflection or autoregressive coefficients has almost no effect on bias and variance of the corresponding estimators. Averaging of reflection coefficients is most robust to segmenting, and is therefore recommended for applications using fixed hardware implementations of the Burg algorithm. View full abstract»

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  • Shape-gain matrix quantizers for LPC speech

    Page(s): 1427 - 1439
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    It has been recently demonstrated that the principles of vector quantization for LPC speech can be simply extended to encompass matrices of LPC vectors with significant savings in bit rate. Unfortunately, however, such locally optimal matrix quantizers have prohibitively high complexity and memory requirements when implemented in a speech vocoder at bit rates giving acceptable quality speech. One approach to solving the problem is to separately code gain and shape in the matrix quantizer. This paper generalizes the principles of shape-gain vector quantizer design for LPC speech to matrix quantization and investigates the properties of the resulting quantizers. In particular, we present a design which combines shape matrices consisting of N shape vectors with K-dimensional gain vectors, where N and K are small integers, in practice, withK geq N. Experimental results show that with K,N geq 3, significant reductions in bit rate over locally optimal vector quantizers are obtained for comparable performance. Simulations indicate that a shape-gain matrix quantizer, using a 10 bit shape codebook and an 8 bit codebook with K = N = 3 operating at 6 bits/frame for the LPC model, gives speech quality comparable to a locally optimal vector quantizer at 9 bits/frame. The matrix quantizer has somewhat greater than 5.7 times the memory requirement of the above vector quantizer, but less than 2.1 times the complexity. Subjective tests show that the speech from this matrix quantizer is intelligible to native speakers of English. View full abstract»

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  • Modifications to the McClellan, Parks, and Rabiner computer program for designing higher order differentiating FIR filters

    Page(s): 1671 - 1674
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    Simple modifications to the McClellan, Parks, and Rabiner linear phase finite impulse response (FIR) filter design program are suggested to allow the design of an nth-order differentiating FIR filter of arbitrary length for any n. Two illustrative examples are also provided. View full abstract»

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  • On the complex residue arithmetic system (CRNS)

    Page(s): 1675 - 1677
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    Recently, a number of papers has been published on the subject of performing complex arithmetic in the residue number system. Methods have been proposed which reduce the arithmetic complexity of a complex multiply by more than 50 percent. In this correspondence it is shown that these methods achieve Winograd's lower bound, and how these efficient mappings can be derived in terms of polynomial rings. View full abstract»

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  • A rearranged DFT algorithm requiring N2/6 multiplications

    Page(s): 1658 - 1659
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    We consider the problem of computing the DFT and present two reductions over the standard formula. In the special case of an N-point sequence with N = 2l, the number of multiplications per output point required by this algorithm is, at most, N/4 - 1 and, on the average, N/6 - 1. Each output point requires no more than N - 1 additions. In applications requiring only some of the output points, a computational savings over the standard (FFT) techniques may be achieved. Furthermore, we argue that in a certain sense these reductions are optimal. View full abstract»

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  • L1deconvolution and its application to seismic signal processing

    Page(s): 1655 - 1658
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    This correspondence reviews fast iterative reweighted least squares (IRLS) and residual steepest descent (RSD) algorithms for Lp,1 leq p leq 2, deconvolution. The timing aspects and the implementation of the IRLS algorithm on an array processor are discussed. The effectiveness of L1deconvolution and its insensitivity to noise bursts are illustrated using simple synthetic as well as complex seismic data. Finally, it is shown that the Lpprediction filters in general need not be stable, and that L1solutions predict the possibility of nonminimum phase aspects of a given set of data. View full abstract»

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  • Form-invariant linear filtering: Theory and applications

    Page(s): 1612 - 1628
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    The form-invariant filters are, by definition, those shift-variant filters such that their output, for any given input, turns out to be linearly scaled (implying that its "form" does not change) whenever the input is linearly scaled. In this paper the most general classes of 1- D and 2-D linear form-invariant filters are derived and their properties are discussed, together with their implementation techniques. Two main implementation approaches are considered: one based on the Mellin transform, the other on a combination of coordinate mappings and shift-invariant filtering. The paper also discusses the related works of other authors covering quite different fields such as optical pattern recognition, image restoration and image reconstruction from projections, radar signal processing, etc. It is shown that the mathematics of form-invariant filtering provides a common framework, if not a powerful unified approach, to the many signal processing techniques spread in the above-mentioned works and apparently different application areas. The paper ends with a processing example showing the usefulness of form-invariant filtering in a pattern recognition problem, that is, in the area where the most promising applications of such a filtering are foreseen. View full abstract»

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  • Hyperbolic householder transformations

    Page(s): 1589 - 1602
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    A class of transformation matrices, analogous to the Householder matrices, is developed, with a nonorthogonal property designed to permit the efficient deletion of data from least-squares problems. These matrices, which we term hyperbolic Householder, are shown to effect deletion, or simultaneous addition and deletion, of data with much less sensitivity to rounding errors than for techniques based on normal equations. When the addition/deletion sets are large, this numerical robustness is obtained at the expense of only a modest increase in computations, and when only a relatively small fraction of the data set is modified, there is a decrease in required computations. Two applications to signal processing problems are considered. First, these transformations are used to obtain a square root algorithm for windowed recursive least-squares filtering. Second, the transformations are employed to implement the rejection of spurious data from the weight vector estimator in an adaptive array. View full abstract»

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  • Parametric projection filter for image and signal restoration

    Page(s): 1643 - 1653
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    A class of linear algebraic restoration filters is derived for the linear degradation model with additive noise. The filters are based on an optimization criterion involving two effects: first, the error in the restored noiseless image compared to the original image, and second, the energy of additive noise passed through the restoration. These two effects can be balanced with a scalar parameter. For both error types, explicit expressions are derived in terms of the parameter. It is shown that by allowing the first error norm to grow slightly, the noise energy may be considerably reduced. This also has a bearing on the analysis of the behavior of other linear parametric restoration filters. View full abstract»

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  • A Kalman filtering approach to short-time Fourier analysis

    Page(s): 1493 - 1501
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    The problem of estimating time-varying harmonic components of a signal measured in noise is considered. The approach used is via state estimation. Two methods are proposed, one involving pole-placement of a state observer, the other using quadratic optimization techniques. The result is the development of a new class of filters, akin to recursive frequency-sampling filters, for inclusion in a parallel bank to produce sliding harmonic estimates. Kalman filtering theory is applied to effect the good performance in noise, and the class of filters is parameterized by the design tradeoff between noise rejection and convergence rate. These filters can be seen as generalizing the DFT. View full abstract»

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  • A convergence analysis of a passive underwater tracking system with nonlinear feedback

    Page(s): 1401 - 1409
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    The basic signal processing structure of a new passive underwater tracking system incorporating nonlinear feedback [1], [2] is modeled, and its ability to converge to an unbiased estimate of target range is examined. Target measurements are derived using difference of arrival times between passive sensor systems geometrically separated. The range tracking system utilizes a nonlinear signal processor to first linearize and invert the noisy time delay measurements. This eliminates the need for extended Kalman filtering techniques and allows the use of a more basic-type state estimator [1]. However, the processed measurements now contain both nonstationary and non-Gaussian measurement errors. To help compensate for these effects, the tracking system incorporates nonlinear feedback from output to input, in order to help maintain a zero-mean measurement error process. Thus, a theoretical investigation is necessary to examine overall tracking system convergence after an initial target detection and/or target maneuver has occurred. The convergence analysis is performed using two separate tracking models. The first model is a scalar first-order low-pass filter. The second model is a vector Kalman-type state estimator. Although the estimator is linear, the overall tracking system is nonlinear due to the non-linear bias removal feedback and the data linearization system. This inherent complexity requires the convergence analysis to be both analytic and make extensive use of computer simulation analysis. Results show that each system converges, but with a small bias that is both geometry and signal-to-noise ratio dependent. View full abstract»

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  • Bandpass and bandstop recursive filters with low sensitivity

    Page(s): 1485 - 1492
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    In a sharp cutoff bandpass or bandstop recursive digital filter, the poles and zeros are expected to be clustered around the band edges, and in close proximity of the unit circle in the z-plane. Implementation of such filters with low sensitivity could be achieved by cascading second-order sections, with appropriate low sensitivity structures chosen for individual sections depending on the locations of the poles. An alternative approach for design and implementation of band-pass/bandstop filter with low sensitivity is shown to be based on transformation of sensitivity characteristics of a low sensitivity structure to appropriate angular regions on the unit circle. A fourth-order section of such a filter is realized with a level of sensitivity which is the same as that of the prototype second-order low-pass filter section realized with low sensitivity and with reduced number of multiplications. A few transformations along with their respective structures for transforming a prototype low-pass filter are given. View full abstract»

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  • On the design and implementation of shift-varying filters

    Page(s): 1665 - 1666
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    This correspondence concerns a particular method for the design and implementation of shift-varying filters for the purpose of approximating, from a finite segment of a data sequence, the response of a shift-invariant filter to the infinite length data sequence. View full abstract»

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  • Sequences with positive semidefinite Fourier transforms

    Page(s): 1502 - 1510
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    A sequence is said to be positive if its Fourier transform exclusively takes on real nonnegative values as a function of frequency. Positive sequences play a prominent role in contemporary signal processing and system theory. To illustrate this point, it is well known that the factorization theorem is extensively used in studies related to wide-sense stationary random signals and linear systems. The ability to appropriately factorize a Fourier transform is contingent on that transform being positive semidefinite. This paper is partially tutorial in that some fundamental positive sequence properties found in dispersed sources are first reviewed. This is followed by the development of several new properties. These properties are in turn used to develop an efficient algorithm for finding that positive sequence which lies closest to a given nonpositive sequence in the least-squares error sense. Interest in this approximation problem arises from the fact that although a given sequence may be theoretically positive, practical considerations often result in its realization being nonpositive. For instance, unbiased autocorrelation lag estimates can lead to nonpositive spectral density function estimates. View full abstract»

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  • On an adaptive noise cancellation application for radar

    Page(s): 1654 - 1655
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    The purpose of this correspondence is to introduce a novel application of adaptive noise cancellation related to a class of radar signals. View full abstract»

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  • Rotational search methods for adaptive Pisarenko harmonic retrieval

    Page(s): 1550 - 1565
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    Two adaptation algorithms for adaptive Pisarenko harmonic retrieval are described. They are derived by considering the associated minimum eigenvalue problem as an optimization problem which seeks the minimum of a quadratic cost function given a hyperspherical constraint. An iterative search procedure is used in which each search path is constrained to lie on the unit hypersphere. Computational complexity per iteration is approximately one-third that of previous adaptive PHR algorithms. Simulations reveal that at low SNR the trial eigenvector can converge to the true minimum eigenvector of the sample covariance matrix, long before this matrix is a good estimate of the true covariance matrix. 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