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Acoustics, Speech and Signal Processing, IEEE Transactions on

Issue 4 • Date Apr 1988

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Displaying Results 1 - 22 of 22
  • Binary multiplication with PN sequences

    Publication Year: 1988 , Page(s): 603 - 606
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (356 KB)  

    It is shown how an nth-order PN sequence can be used to multiply two n/2-bit numbers to n-bit accuracy. Hardware implementation for digital filtering is also discussed View full abstract»

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  • Design of optimum discrete finite duration orthogonal Nyquist signals

    Publication Year: 1988 , Page(s): 606 - 608
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (272 KB)  

    A design technique is presented for constructing a discrete finite duration signal having a spectral energy that is maximized in a given band. The method involves the minimization of a constrained nonlinear functional by the method of steepest descent. The constructed signal is orthogonal to the block-shifted version of itself View full abstract»

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  • Performance contours of autoregressive estimates

    Publication Year: 1988 , Page(s): 608 - 610
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (240 KB)  

    Given a pth-order AR (autoregressive) process, the authors obtain an explicit formula that relates the true parameters and their estimates (or distorted versions) to the variance of the prediction error process. For a second-order AR model they determine contours within which the parameter estimates must lie, corresponding to a set of prediction error variance values. Conversely, for a given set of parameter estimates, they obtain contours within which the true parameters would lie, corresponding to a set of prediction error variance values. These results provide some interesting insights about AR modeling View full abstract»

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  • DFT spectrum filtering

    Publication Year: 1988 , Page(s): 461 - 470
    Cited by:  Papers (5)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (716 KB)  

    A.E. Kahveci and E.L. Hall (see IEEE Trans. Comput., vol.C-23, no.9, p.976-81, Sept. 1974) introduced the concept of filtering discrete Fourier transform (DFT) spectra in the Walsh sequency domain. This is accomplished by finding a real and block-diagonal Walsh filter matrix Gw in the Walsh domain that performs the sample filtering operation as the prototype complex diagonal Fourier filter matrix Gf in the Fourier domain. The present authors provide additional information on the structure of Gw and generalize some results by C.J. Zarowski and M. Yunik (see ibid., vol.ASSP-33, p.1246-52, Oct. 1985). They consider a more general class of transforms, the T transforms, and the structure of the resulting T transform filter matrices G t. Examples of T besides T=W are considered, such as the Harr transform and fourth-order Chrestenson transform. The implementation of the presented DFT spectrum filtering techniques using linear systolic arrays is briefly considered View full abstract»

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  • An improved spatial smoothing technique for bearing estimation in a multipath environment

    Publication Year: 1988 , Page(s): 425 - 432
    Cited by:  Papers (111)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (624 KB)  

    It is well known that signal subspace algorithms perform poorly when coherent or highly correlated signals are present. Recently, the so-called spatial smoothing technique was devised to preprocess the array covariance matrix so that signal subspace algorithms can be applied irrespective of the signal correlation. Unfortunately, the application of this technique reduces the effective aperture of the array. A modified spatial smoothing technique that is capable of increasing the effective aperture of the array over that of conventional spatial smoothing methods is explored. It is shown that under certain conditions, the modified algorithm may fail to yield the desired increase in array aperture, and some simulation results concerning the sensitivity of the modified spatial smoothing algorithm to these conditions are provided View full abstract»

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  • Autoregressive spectral estimation in additive noise

    Publication Year: 1988 , Page(s): 490 - 501
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (712 KB)  

    The estimation of the spectral density of a discrete-time stationary Gaussian autoregressive process AR (p) from a finite set of noise observations is considered. A modified spectral estimator based on the high-order Yule-Walker equations is considered. Joint asymptotic normality of this spectral estimator is established; a precise asymptotic expression for the covariance matrix of the limiting distribution is obtained. The special case of AR(1) plus noise is considered in some detail View full abstract»

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  • Maximum a posteriori estimation of time-varying ARMA processes from noisy observations

    Publication Year: 1988 , Page(s): 471 - 476
    Cited by:  Papers (9)  |  Patents (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (468 KB)  

    The estimation of the parameters of discrete-time autoregressive moving-average (ARMS) processes observed in white noise is considered. A class of time-varying ARMA processes in which the parameter process is the output of a known linear system driven by white Gaussian noise is examined. The maximum a posteriori (MAP) estimator is defined for the trajectory of the parameter's random process. A two-step estimate-and-maximize (EM)-based (E-step and M-step) iterative algorithm is derived. The posterior probability of the parameters is increased in each iteration, and convergence to stationary points of the posterior probability is guaranteed. Each iteration involves two linear systems and is easily implemented. It is shown that similar results can be obtained for a wide class of parameter estimation problems View full abstract»

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  • Recursive covariance ladder algorithms for ARMA system identification

    Publication Year: 1988 , Page(s): 560 - 580
    Cited by:  Papers (16)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1372 KB)  

    The recently developed method of pure-order recursive ladder algorithms (PORLA) is extended to facilitate the identification of autoregressive moving-average (ARMA) models. Since the time recursion in this method is limited in the calculation of the input data covariance matrix, roundoff errors cannot propagate in time in higher stages of the pure-order recursively constructed ladder form. Thus, the superior least-squares tracking and fast start-up capability of the proposed algorithms is not corrupted by roundoff error. Furthermore, the algorithms allow the use of higher-order recursive windows on the data (e.g., recursive Hanning), which again significantly improves the tracking as well as the steady-state behavior. A computer program, an instructive example for implementation of the method on a massively parallel processor, and several experimental results which confirm the superior properties of the PORLA method over conventional techniques are shown View full abstract»

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  • A new method for the design of two-dimensional recursive digital filters

    Publication Year: 1988 , Page(s): 589 - 598
    Cited by:  Papers (6)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (776 KB)  

    A method for designing general two-dimensional digital recursive filters is presented. This method involves an iterative optimization procedure that is based on a modified Marquardt algorithm. The error function to be minimized is composed of the real part and imaginary part of the frequency response error and of stability error that is computed in the spatial domain. Analytic formulas for the derivatives of error function with respect to filter coefficients are derived, so the error derivatives requires in the optimization procedure can be calculated analytically rather than numerically. This results in saving design time significantly. Several examples that demonstrate the capability and the efficiency of this method are given View full abstract»

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  • Cepstral domain talker stress compensation for robust speech recognition

    Publication Year: 1988 , Page(s): 433 - 439
    Cited by:  Papers (39)  |  Patents (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (560 KB)  

    A study of talker-stress-induced intraword variability and an algorithm that compensates for the systematic changes observed are presented. The study is based on hidden Markov models trained by speech tokens spoken in various talking styles. The talking styles include normal speech, fast speech, loud speech, soft speech, and taking with noise injected through earphones; the styles are designed to simulate speech produced under real stressful conditions. Cepstral coefficients are used as the parameters in the hidden Markov models. The stress compensation algorithm compensates for the variations in the cepstral coefficients in a hypothesis-driven manner. The functional form of the compensation is shown to correspond to the equalization of spectral tilts. Substantial reduction of error rates has been achieved when the cepstral domain compensation techniques were tested on the simulated-stress speech database. The hypothesis-driven compensation technique reduced the average error rate from 13.9% to 6.2%. When a more sophisticated recognizer was used, it reduced the error rate from 2.5% to 1.9% View full abstract»

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  • Some properties of prediction and interpolation errors

    Publication Year: 1988 , Page(s): 525 - 531
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (568 KB)  

    Linear prediction and interpolation are widely used in signal processing. Usually they are obtained by linear filters of finite memory, and the prediction and interpolation errors decrease when this memory increases. If the asymptotic values of these errors are null, the signal is singular. Some theoretical aspects of finite and infinite prediction and interpolation are discussed, and a numerical method to compute the prediction and interpolation errors for signals with a given power spectrum is presented. A large number of results obtained with this method are discussed, especially the difference between regular and singular signals. This method also allows the behavior of prediction and interpolation errors when the memory of the filter increases to be studied View full abstract»

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  • On the higher order distributions of speech signals

    Publication Year: 1988 , Page(s): 602 - 603
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (176 KB)  

    Experimental results are presented that provide some insight into the higher-order statistical properties of speech signals. It is demonstrated that the third- and fourth-order distributions of the speech signal can be considered as mixtures of, rather than single, spherically invariant (Gaussian) distributions, where the mixing is controlled by the history of the process View full abstract»

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  • Delay replacement in direct form structures

    Publication Year: 1988 , Page(s): 453 - 460
    Cited by:  Papers (30)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (472 KB)  

    A class of high-order low-sensitivity digital filter structures is generated by means of a direct transformation of a high-order direct-form (DF) structure. The proposed structures have low coefficient dynamic range, and exhibit low coefficient frequency-response sensitivity and roundoff noise characteristics, yet require only 3N multiplications per output sample as compared to 2N in the direct-form case View full abstract»

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  • Correlation matching by finite length sequences

    Publication Year: 1988 , Page(s): 545 - 559
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1128 KB)  

    The problem of matching a finite length data sequence to a set of (not necessarily uniformly spaced) correlation lags is considered. A characterization of the set of correlations that can be derived from sequences of a given length is presented. Using this characterization, an algorithm called the expanding hull algorithm is presented for determining the minimum sequence length, and a sequence of this length, which matches a given set of correlation values, is obtained. This sequence has a Z transform which is the lowest-order correlation matching moving average model. The sequence also generates the minimum length correlation extension. The expanding hull algorithm also provides a method for extendibility testing of missing lag and multidimensional correlation sequences. Numerical examples are provided View full abstract»

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  • Order selection for AR models by predictive least squares

    Publication Year: 1988 , Page(s): 581 - 588
    Cited by:  Papers (14)  |  Patents (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (420 KB)  

    A criterion is presented for selecting the order of autoregressive models that, unlike the existing criteria, is amenable to online or adaptive operation. It is based on the predictive least squares (PLS) principle and is implemented in a computationally efficient way by predictive lattice filters. The consistency of the criterion is proved, and its performance is demonstrated by computer simulations. Assuming the data to be generated by an AR model of order p, the order selection criterion should select the correct order p with probability that converges to 1 as the sample size grows to infinity. It is proved that the PLS criterion is indeed consistent, thereby giving a solid justification for the criterion. Simulation results that demonstrate the performance of the PLS criterion in comparison to H. Akaike's AIC (1973) and the MDL criteria of J. Rissanen (1978) and G. Schwarz (1978) are given View full abstract»

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  • Adaptive time delay estimation with constraints

    Publication Year: 1988 , Page(s): 599 - 602
    Cited by:  Papers (11)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (268 KB)  

    The time (shift) delay parameter between two signals is modeled as a finite-impulse response filter whose coefficients are samples of a sinc function. The time-domain LMS (least-mean-squares) adaptive algorithm is used, but only the weight with the largest magnitude is updated, which involves less computation. The result is a faster adaptation and the elimination of interpolation needed in previous approaches to obtain nonintegral (multiples of sampling period) time-delay estimates View full abstract»

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  • On error-saturation nonlinearities in LMS adaptation

    Publication Year: 1988 , Page(s): 440 - 452
    Cited by:  Papers (43)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (796 KB)  

    The effect of a saturation-type error nonlinearity in the weight update equation in least-mean-squares (LMS) adaptation is investigated for a white Gaussian data model. Nonlinear difference equations are derived for the eight first and second moments, which include the effect of an error function (erf) saturation-type nonlinearity on the error sequence driving the algorithm. A nonlinear difference equation for the mean norm is explicitly solved using a differential equation approximation and integration by quadratures. The steady-state second-moment weight behavior is evaluated exactly for the erf nonlinearity. Using the above results, the tradeoff between the extent of error saturation, steady-state excess mean-square error, and rate of algorithm convergence is studied. The tradeoff shows that (1) starting with a sign detector, the convergence rate is increased by nearly a factor of two for each additional bit, and (2) as the number of bits is increased further, the additional bit by very little in convergence speed, asymptotically approaching the behavior of the linear algorithm View full abstract»

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  • Analysis of the asymptotic relative efficiency of the MUSIC algorithm

    Publication Year: 1988 , Page(s): 532 - 544
    Cited by:  Papers (97)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (804 KB)  

    An analytical performance evaluation of the errors of the direction-of-arrival estimates obtained by the MUSIC algorithm for uncorrelated sources is provided. Explicit asymptotic formulas are derived for the means and the covariance of the estimates. The covariances are then compared to the Cramer-Rao lower bound. It is shown that for a single course, the MUSIC algorithm is asymptotically efficient. For multiple sources, the algorithm is not efficient in general. However, it approaches asymptotic efficiency when the SNRs (signal-to-noise ratios) of all sources tend to infinity. It is illustrated by several test cases that the relative efficiency of the MUSIC algorithm is nearly one under a wide range of parameter variations. The analytic performance evaluation thus confirms empirical evidence to the excellent performance of the MUSIC algorithm for narrowband signals View full abstract»

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  • ARMA bispectrum approach to nonminimum phase system identification

    Publication Year: 1988 , Page(s): 513 - 524
    Cited by:  Papers (37)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (716 KB)  

    An identification procedure is proposed for a nonGaussian white-noise-driven, linear, time-invariant, nonminimum-phase FIR (finite-impulse response) system. The method is based on parametric modeling of the third moments of the output sequence and uses causal and anticausal autoregressive moving-average (ARMA) models. The magnitude and phase response of the system are expressed in terms of the AR parameters of the ARMA models. In particular, the AR part of the causal ARMA model captures the minimum-phase component of the system, and the AR part of the anticausal ARMA captures the maximum-phase component. Both sets of parameters are obtained by solving overdetermined linear systems of equations. A model-order-selection criterion based on third-order moments is proposed. The ARMA bispectrum approach is compared to more conventional approaches for magnitude and phase reconstruction. It is demonstrated that the proposed identification procedure exhibits improved modeling performance. The method does not require knowledge of the non-Gaussian noise distribution View full abstract»

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  • A generalized approach to direction finding

    Publication Year: 1988 , Page(s): 610 - 612
    Cited by:  Papers (14)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (168 KB)  

    With reference to the recently developed estimation of signal parameters using a rotational invariance techniques (ESPRIT) it is pointed out that other operators can be used to generate the matrix pencil. By way of example, the moving window operator is discussed View full abstract»

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  • Application of the lattice filter to robust estimation of AR and ARMA models

    Publication Year: 1988 , Page(s): 502 - 512
    Cited by:  Papers (21)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (880 KB)  

    A method for estimating the parameters of an autoregressive moving-average (ARMA) model is proposed. A (p+q)-stage lattice whitening filter is used to obtained consistent estimates of the AR parameters of an ARMA (p,p) model. It is shown that a set of MA parameter estimates can also be obtained using this approach with little added computation. The method is used for estimation of the AR parameters of an ARMA process in additive white noise. It is shown that the lattice filter is also useful in robust estimation of the AR parameter on an ARMA process with additive outliers View full abstract»

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  • Parameter estimation of superimposed signals using the EM algorithm

    Publication Year: 1988 , Page(s): 477 - 489
    Cited by:  Papers (274)  |  Patents (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (820 KB)  

    A computationally efficient algorithm for parameter estimation of superimposed signals based on the two-step iterative EM (estimate-and-maximize, with an E step and an M step) algorithm is developed. The idea is to decompose the observed data into their signal components and then to estimate the parameters of each signal component separately. The algorithm iterates back and forth, using the current parameter estimates to decompose the observed data better and thus increase the likelihood of the next parameter estimates. The application of the algorithm to the multipath time delay and multiple-source location estimation problems is considered 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