A fixed point computation of partial correlation coefficients

Roux, J. Le; Gueguen, C.

doi:10.1109/tassp.1977.1162944

Cited by 126 publications

(11 citation statements)

References 5 publications

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“…An alternate method -Leroux Gueguen Algorithm (LGA) eliminates the problem related to dynamic range in a fixed-point environment by taking the application of Schwartz inequality in computation of this method [13]. This technique also reduces the computational time by avoiding the larger matrix inversion as in Normal Equation (9).…”

Section: Modified Leroux Gueguen Techniquementioning

confidence: 99%

On the optimal frame size of linear prediction techniques

Khan

et al. 2013

2013 International Conference on Circuits, Power and Computing Technologies (ICCPCT)

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Loss of data in Communication Systems is a major problem. The main sources of data loss include congestion, sensor faults, mismatch of measuring devices and limited memory of the buffer registers. Three linear prediction (LP) techniques i.e. Normal Equation method, Levinson Durbin Algorithm (LDA) and Leroux Gueguen Algorithm (LGA) are used to predict the missing data. The objective of this paper is to analyze the three linear prediction techniques for both internal and external prediction and the effect of frame size on different parameters i.e. computational time, prediction gainand prediction error is also being analyzed. The LP algorithms are modified and a threshold limit for prediction error is set to keep the error bounded. A sub-optimal frame size is selected for all the three LP methods and in the end the three algorithms are compared for efficient performance.

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Section: Modified Leroux Gueguen Techniquementioning

confidence: 99%

On the optimal frame size of linear prediction techniques

Khan

et al. 2013

2013 International Conference on Circuits, Power and Computing Technologies (ICCPCT)

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“…The first fast Toeplitz solver of this type was proposed by Bareiss [2]. Later on, closely related algorithms were presented in [27,29,31]. Today, Toeplitz solvers based on (1.4) are usually referred to as Schur-type algorithms because of their intimate connection [1,25] with Schur's seminal work [32].…”

Section: Classical Fast Toeplitz Solversmentioning

confidence: 99%

A look-ahead Bareiss algorithm for general Toeplitz matrices

Freund

1994

Numerische Mathematik

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The Bareiss algorithm is one of the classical fast solvers for systems of linear equations with Toeplitz coefficient matrices. The method takes advantage of the special structure, and it computes the solution of a Toeplitz system of order N with only O(N 2 ) arithmetic operations, instead of O(N 3 ) operations. However, the original Bareiss algorithm requires that all leading principal submatrices be nonsingular, and the algorithm is numerically unstable if singular or ill-conditioned submatrices occur. In this paper, an extension of the Bareiss algorithm to general Toeplitz systems is presented. Using look-ahead techniques, the proposed algorithm can skip over arbitrary blocks of singular or ill-conditioned submatrices, and at the same time, it still fully exploits the Toeplitz structure. Implementation details and operations counts are given, and numerical experiments are reported. We also discuss special versions of the proposed look-ahead Bareiss algorithm for Hermitian indefinite Toeplitz systems and banded Toeplitz systems. Subject Classification (1991): 65F05, 15A57, 42C05 Mathematics

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“…The reflection (PARCOR) coefficients {kk } are computed from the set of normalized autocorrelation coefficients {rk} via the Le Roux-Gueguen algorithm suitable for a fixed point implementation (Le Roux and Gueguen 1977). The set of autoregressive (prediction) coefficients {ak } is evaluated iteratively using the recursion formula based on the well-known Levinson-Durbin's algorithm (Makhoul 1975):…”

Section: Processing Strategymentioning

confidence: 99%

An application of mapping neural networks and a digital signal processor for cochlear neuroprostheses

Zadák

Unbehauen

1993

Biol. Cybern.

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Cochlear neuroprostheses strive to restore the sensation of hearing to patients with a profound sensorineural deafness. They exhibit a stimulation of the surviving auditory nerve neurons by electrical currents delivered through electrodes placed on or within the cochlea. The present article describes a new method for an efficient derivation of the required information from the incoming speech signal necessary for the implant stimulation. Also some realization aspects of the new approach are addressed. In the new strategy, a multilayer neural network is employed in the formant frequency estimation having some suitable speech signal descriptors as particular input signals. The proposed method allows us a fast formant frequency estimation necessary for the implant stimulation. With the developed strategy, the prosthesis can be adjusted to the environment which the patient is supposed to live in. Moreover, the neural network concept offers us an alternative for dealing with the areas of neural loss or "holes" in the frequency map of the patient's ear.

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A fixed point computation of partial correlation coefficients

Cited by 126 publications

References 5 publications

On the optimal frame size of linear prediction techniques

On the optimal frame size of linear prediction techniques

A look-ahead Bareiss algorithm for general Toeplitz matrices

An application of mapping neural networks and a digital signal processor for cochlear neuroprostheses

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