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Acoustics, Speech and Signal Processing, IEEE Transactions on

Issue 3 • Date June 1986

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Displaying Results 1 - 25 of 38
  • [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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  • Practical supergain

    Page(s): 393 - 398
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    The problem considered is that of designing endfire line array shadings which provide a useful amount of supergain without extreme sensitivity to random errors. Optimum shading weights are obtained subject to a constraint on the gain against uncorrelated white noise. The results of optimum array gain versus white noise gain constraint are presented parametrically for arrays of different interelement spacings, and different noise fields. Results are presented for spherically and cylindrically isotropic noise, and other wavenumber limited noise fields, used in modeling ocean ambient noise. It is found that nearly optimum performance can be obtained in a simple delay and sum beamformer by shading to reduce sidelobes and modest oversteering to reduce mainlohe width without too large a reduction in mainlobe sensitivity. View full abstract»

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  • A normalized frequency domain LMS adaptive algorithm

    Page(s): 452 - 461
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    A scheme is presented for obtaining an input power estimate for setting the algorithm gain parameter μ separately in each frequency bin in the frequency domain LMS adaptive algorithm. This is particularly important if the input has large spectral variations, and a single feedback parameter, set on the broad-band power, could result in instability in the adaptive filters in some frequency bins. The estimate is incorporated directly into the algorithm as a data dependent time-varying stochastic μ(n). Using a Gaussian data model and sample-to-sample data independence, first-order linear difference equations are derived and solved for the mean and misadjustment errors. The performance of the scheme is compared to the case for which the input power level is known a priori. For the same transient response, only about ten samples need be averaged to yield the same misadjustment error. View full abstract»

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  • Algebraic approach to system identification

    Page(s): 462 - 469
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    A variety of identification procedures exists for estimating the parameters of an autoregressive moving-average (ARMA) process from noise-free excitation and noise-contaminated response data. In this paper, an identification procedure is proposed for the more realistic situation in which both the excitation and response are contaminated by white noises. The method is based upon the null space characterization of an associated "data matrix." Some of the more important algebraic properties possessed by this data matrix are first established in the ideal noise-free data case. In particular, it is found that an overordering of the ARMA model will not impair the identification of the Underlying system. In the more realistic noise-contaminated data case, an approximation of the data matrix's null space is affected by using an eigenvalue-eigenvector decomposition. By incorporating this null space approximation, the deleterious effects of the noise are significantly reduced thereby giving rise to improved modeling performance. This improvement is demonstrated by means of a standard example in which the proposed identification method is shown to produce a better modeling behavior than does the classical least-squares method, the corresponding iterative generalized least-squares method, and a commonly employed instrumental variable method. View full abstract»

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  • Information theory measures with application to model identification

    Page(s): 511 - 517
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    The identification of the order of a model which is fitted to data is of central importance. We have considered in this paper an information theoretic criterion, the AIC, which has found considerable use in many diverse applications. Our discussion is presented with various points in mind. First of all, since the theory of the AIC appears in the statistical literature in a complex form, we have attempted to present the salient points of the development in a simplified manner coupled with a geometrical interpretation which we hope will illuminate the nature of this criterion. Furthermore, we have illustrated the development with an analytical example and have compared the AIC to other measures which have been proposed in the literature. Diverse applications of the AIC, which we have investigated, include time series modeling, parametric inverse problems, and spectral analysis. We have found the AIC to be both a versatile and, what is particularly important, a robust criterion. View full abstract»

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  • Performance of transform-domain LMS adaptive digital filters

    Page(s): 499 - 510
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    In this paper we analyze the performance, particularly the convergence behavior, of the transform-domain least mean-square (LMS) adaptive digital filter (ADF) using the discrete Fourier transform and discrete orthogonal transforms such as discrete cosine and sine transforms. We first obtain the optimum Wiener solution and the minimum mean-squared error (MSE) in the transform domain. It is shown that the two minimum MSE's in the time and transform domains are identical independently of the transforms used. We then study the convergence conditions and the steady-state excess MSE's of the transform-domain LMS (TRLMS) algorithms both for the cases of having a constant and a time-varying convergence factors. When a constant convergence factor is used, the convergence behaviors of the LMS and TRLMS ADF's appear to be almost identical, provided that each has an appropriate value of the convergence factor depending on the transform used. Also, based on the concept of a self-orthogonalizing algorithm in the transform domain, it is shown that the convergence speed of the TRLMS ADF can be improved significantly for the same excess MSE as that of the LMS ADF. In addition, we compare the computational complexities of the LMS and TRLMS ADF'S. Finally, we investigate by computer simulation the effects of system parameter values and different transforms on the convergence behavior of the TRLMS ADF. View full abstract»

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  • Adaptive nonlinear digital filters using distributed arithmetic

    Page(s): 518 - 526
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    A nonlinear adaptive filter structure, based upon the theory of the truncated discrete Volterra series, is presented. A memory-oriented implementation exploiting distributed arithmetic is considered, and the conventional LMS adaptation algorithms are suitably modified. Memory-size reduction methods are developed to obtain simpler actual realizations. Computer simulation results are presented. View full abstract»

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  • An algorithm for pole-zero modeling and spectral analysis

    Page(s): 637 - 640
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    An explicit connection between fitting exponential models and pole-zero models to observed data is made. The fitting problem is formulated as a constrained nonlinear minimization problem. This problem is then solved using a simplified iterative algorithm. The algorithm is applied to simulated data, and the performance of the algorithm is compared to previous results. View full abstract»

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  • Adaptive identification of a time-varying ARMA speech model

    Page(s): 423 - 433
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    We propose an adaptive algorithm to estimate time-varying ARMA parameters for speech signals. It estimates both input excitations and underlying system parameters. The proposed algorithm is an extended form of the Kalman filter algorithm. We assume the input is either a white Gaussian process or a pseudoperiodical pulse-train as commonly adopted in LPC processing. The time variation of parameters is monitored by a likelihood function. In order to estimate optimal parameters in a small amount of data, AR and MA orders of an estimator are set to be higher than those of a true system. Parsimonious ARMA parameters are calculated from parameters obtained by the high-order ARMA model. Examples of synthetic and real speech sounds are given to demonstrate the tracking ability of this algorithm. View full abstract»

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  • A real-time two-dimensional moment generating algorithm and its single chip implementation

    Page(s): 546 - 553
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    We present a fast algorithm and its single chip VLSI implementation for generating moments of two-dimensional (2-D) digital images for real-time image processing applications. Using this algorithm, the number of multiplications for computing 16 moments of a 512 × 512 image is reduced by more than 5 orders of magnitude compared to the direct implementation; the number of additions is reduced by a factor of 4. This also makes the software implementation extremely fast. Using the chip, 16 moments μp,q(p = 0, 1, 2, 3, q = 0, 1, 2, 3) of a 512 × 512 8 bits/pixel image can be calculated in real time (i.e., 30 frames per second). Each moment value is computed as a 64- bit integer. The basic building block of the algorithm is a single-pole digital filter implemented with a simple accumulator. These filters are cascaded together in both horizontal and vertical directions in a highly regular structure which makes it very suitable for VLSI implementation. The chip has been implemented in 2.5 μ CMOS technology, it occupies 6100 μm × 6100 μm of silicon area. The chip can also be used as a general cell in a systolic architecture for implementing 2-D transforms having polynomial basis functions. View full abstract»

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  • Time-varying filtering and signal estimation using Wigner distribution synthesis techniques

    Page(s): 442 - 451
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    The short-time Fourier transform (STFT), the ambiguity function (AF), and the Wigner distribution (WD) are mixed time-frequency signal representations that use Fourier transform techniques to map a one-dimensional function of time into a two-dimensional function of time and frequency. These mixed time-frequency mappings have been used to analyze the local frequency characteristics of a variety of signals and systems. Although much work has also been done to develop STFT and AF synthesis algorithms that can be used to implement a variety of time-varying signal processing operations, no such synthesis techniques have thus far been developed for the WD. In this paper, a signal synthesis algorithm that works directly with the real-valued high-resolution WD will be derived. Examples of how this WD synthesis procedure can be used to perform time-varying filtering operations or signal separation will be given. View full abstract»

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  • A new algorithm for speech fundamental frequency estimation

    Page(s): 626 - 630
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    In this correspondence, we present a method of detecting the fundamental frequency based on the recognition of "fundamental peaks" of a low-pass filtered (fc= 0.7 kHz) speech signal. Average overall recognition accuracy of approximately 99 percent for the training speech sample and 97.5 percent for a test speech sample were achieved. The experimental verification of the developed FPR (fundamental peak recognition) method also included an objective comparison to Miller's data reduction method, and a "subjective" performance evaluation using the standard cepstrum method. The FPR classification scheme is computationally efficient and easily implementable with relatively slow 8-bit microprocessors. View full abstract»

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  • A parallel least-squares linear prediction method based on the circular lattice filter

    Page(s): 640 - 642
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    In this correspondence, we propose a parallel least-squares method for linear prediction and estimation based on the circular lattice filter. Instead of minimizing the total sum of prediction errors, the method proceeds by dividing the sum into several parts and then minimizing each part in parallel, and then averaging the resulting estimates to form a final estimate. It is shown that this estimator is asymptotically efficient in the same way as the traditional least-squares estimator. View full abstract»

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  • Sinusoidal disturbance rejection with application to helicopter flight data estimation

    Page(s): 479 - 484
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    This paper is concerned with the problem of eliminating sinusoidal disturbances from data while producing minimal distortion to the underlying data. A particular example of this problem arises in the filtering of helicopter data which are corrupted by sinusoidal disturbances due to rotor motion. It is shown that an optimal solution to the problem can be found using Kalman filtering theory. The properties of the optimal filter are analyzed using recent results on filtering for nonstabilizable systems. These results are then used to motivate a particular near-optimal filter which has enhanced robustness properties relative to the optimal filter. It will also be shown that an identical filter can be derived using recent results on the evaluation of recursive discrete Fourier transforms. This link between time and frequency domain methods leads to a rather complete understanding of the characteristics of the filter. Specific results are presented showing the application of the filter to real helicopter data. View full abstract»

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  • General in-place calculation of discrete Fourier transforms of multidimensional sequences

    Page(s): 565 - 572
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    The computation of the discrete Fourier transform (DFT) of real multidimensional sequences requires an extraordinary amount of computer memory. The in-place calculation of the discrete Fourier transform reduces the required memory and is thus highly desirable. The present paper relies on the conjugate property of the generalized DFT in order to define novel, advantageous algorithms for the in-place calculation of the DFT of multidimensional sequences. View full abstract»

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  • Estimation of phase angles from the cross-spectral matrix

    Page(s): 405 - 422
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    The so-called signal subspace methods in spatial signal processing have been used up to now in the case of steering vectors depending upon very few parameters. For instance, they are used currently for steering vectors of the form (1, e, e2iθ, ... , ei(N-1)θ)Twhich occur in the case of plane waves and a linear array of N equispaced sensors. We show, by providing a suitable algorithm, that these methods can be extended to the general case of steering vectors of the form (1, eiθ2, eiθ3, ... , eiθN)Twhere the phase angles θ2,..., θNare treated as free parameters. This kind of situation occurs, for instance, in the case of randomly distorted wavefronts, and/or flexible antennas of unknown geometry, when the fluctuations of the wavefronts and of the antenna are very slow with respect to the time constants used for the estimation of the cross-spectral matrices. As usual in signal subspace methods, the proposed algorithm works very well when the problem presents a strong degree of overdetermination (many more sensors than sources), and the cross-spectral matrix is estimated with good precision. View full abstract»

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  • Exact reconstruction techniques for tree-structured subband coders

    Page(s): 434 - 441
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    In recent years, tree-structured analysis/reconstruction systems have been extensively studied for use in subband coders for speech. In such systems, it is imperative that the individual channel signals be decimated in such a way that the number of samples coded and transmitted do not exceed the number of samples in the original speech signal. Under this constraint, the systems presented in the past have sought to remove the aliasing distortion while minimizing the overall analysis/reconstruction distortion. In this paper, it is shown that it is possible to design tree-structured analysis/reconstruction systems which meet the sampling rate condition and which result in exact reconstruction of the input signal. The conditions for exact reconstruction are developed and presented. Furthermore, it is shown that these conditions are not overly restrictive and high-quality frequency division may be performed in the analysis section. A filter design procedure is presented which allows high-quality filters to be easily designed. View full abstract»

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  • Fast algorithms for the DFT and other sinusoidal transforms

    Page(s): 642 - 644
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    A new matrix factorization is proposed for DCT-IV, which is the basis of fast algorithms for many sinusoidal transforms. The new fast algorithm for DFT, based on the new factorization, requires the same number of multiplications and far fewer additions than the Preuss algorithm. View full abstract»

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  • An efficient algorithm for computing Pisarenko's harmonic decomposition using Levinson's recursion

    Page(s): 485 - 491
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    The harmonic decomposition of a random process into a sum of sinusoids in white noise is an important problem with applications in a number of different areas. As a result of the work of V. F. Pisarenko, it has been shown that the sinusoidal frequencies and the white noise power are determined by the minimum eigenvalue and the corresponding eigenvector of the autocorrelation matrix. In this paper, an efficient algorithm is presented for finding this eigenvalue and eigenvector. In addition to its being computationally more efficient than the power method, it has a "built-in" criterion for selecting the model order to use in the decomposition. Some examples are presented and the results are compared to those obtained using other approaches. View full abstract»

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  • The discrete Gerchberg algorithm

    Page(s): 624 - 626
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    The discrete version of the Gerchberg algorithm for iterative restoration of a time-constrained function from only partial knowledge of its spectrum (or vice versa) is analyzed. Although convergence is guaranteed, eigenvalues close to unity inhibit iteration to the limit. Identification of these large eigenvalues, allowing extrapolation to the limit, is described. View full abstract»

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  • General problems of minimum-variance recursive waveshaping

    Page(s): 616 - 618
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    In this correspondence, we sum up the general problems of minimum-variance recursive waveshaping (MVRW). We first develop the general structure of minimum-variance waveshaping (MVW) and then present the recursive algorithms of MVRW. Both on-line and off-line algorithms are considered. View full abstract»

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  • Recursive center-frequency adaptive filters for the enhancement of bandpass signals

    Page(s): 633 - 637
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    In some signal enhancement and tracking applications, where a priori information regarding the signal bandwidth and spectral shape is available, it is suggested to use a recursive center-frequency adaptive filter instead of a fully adaptive filter. A new adaptive algorithm, namely, the recursive maximum-mean-squares (RMXMS) algorithm, is developed based on the gradient ascent technique for the implementation of these filters. An adaptation mechanism based on the Gauss-Newton algorithm is also presented. This class of filters is found to have several advantages which include faster convergence and lesser computational complexity compared to the fully adaptive filters. 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