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IEEE Signal Processing Letters

Issue 9 • Sept. 1996

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Displaying Results 1 - 5 of 5
  • Discriminative weighting of HMM state-likelihoods using the GPD method

    Publication Year: 1996, Page(s):257 - 259
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (332 KB)

    We propose a new method of finding discriminative state-weights recursively using the generalized probabilistic descent method. This method is implemented with minor modification of the conventional parameter estimation and recognition algorithms by constraining the sum of the state-weights to the number of states in a recognition unit, and can be applied to continuous speech recognition as well a... View full abstract»

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  • Wiener extrapolation of sequences and the expectation-maximization algorithm

    Publication Year: 1996, Page(s):260 - 262
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (271 KB)

    Sequence extrapolation is a classical problem in signal processing. The application of the expectation-maximization (EM) algorithm to estimate the signal autocorrelation in Wiener extrapolation allows us to view previous techniques as particular cases, to modify them, and to introduce new ones offering better performance with a moderate increase in computational load. View full abstract»

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  • Efficient computation of locally monotonic regression

    Publication Year: 1996, Page(s):263 - 265
    Cited by:  Papers (4)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (246 KB)

    Locally monotonic regression provides a way of smoothing signals under the smoothness criterion of local monotonicity, which sets a restriction on how often a signal may change trend (increasing to decreasing, or vice versa). So far, the applicability of locally monotonic regression has been limited by the high computational costs of the available algorithms that compute them. We present a powerfu... View full abstract»

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  • Sampling requirements for Volterra system identification

    Publication Year: 1996, Page(s):266 - 268
    Cited by:  Papers (36)  |  Patents (6)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (279 KB)

    Volterra systems generally produce-due to nonlinearity-an output signal with a higher frequency range when compared with the input signal. Hence, it seems necessary to sample the input and output signals at twice the maximum frequency of the output signal. The article shows that it is sufficient to sample at twice the maximum frequency of the input signal. A discrete-time Volterra system also prod... View full abstract»

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  • Nearly optimum multilevel block truncation coding based on a mean absolute error criterion

    Publication Year: 1996, Page(s):269 - 271
    Cited by:  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (292 KB)

    A new generalized multilevel block truncation coding (BTC) algorithm based on a mean absolute error criterion is proposed. The proposed multilevel BTC algorithm requires only additions/subtractions to segment a block into desired regions. The simulation results indicate that the mean absolute error and peak signal-to-noise ratio (PSNR) of the algorithm is very close to those of the optimum BTC. Th... View full abstract»

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Aims & Scope

The IEEE Signal Processing Letters is a monthly, archival publication designed to provide rapid dissemination of original, cutting-edge ideas and timely, significant contributions in signal, image, speech, language and audio processing.

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Meet Our Editors

Editor-in-Chief
Peter Willett
University of Connecticut
Storrs, CT 06269
peter.willett@uconn.edu