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13
Regularized Estimation of Mixed Spectra Using a Circular GibbsMarkov Model
 IEEE Trans. Signal Processing
, 2001
"... Formulated as a linear inverse problem, spectral estimation is particularly underdetermined when only short data sets are available. Regularization by penalization is an appealing nonparametric approach to solve such illposed problems. Following Sacchi et al., we first address line spectra reco ..."
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Cited by 13 (3 self)
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Formulated as a linear inverse problem, spectral estimation is particularly underdetermined when only short data sets are available. Regularization by penalization is an appealing nonparametric approach to solve such illposed problems. Following Sacchi et al., we first address line spectra recovering in this framework. Then, we extend the methodology to situations of increasing difficulty: the case of smooth spectra and the case of mixed spectra, i.e., peaks embedded in smooth spectral contributions.
Relationships between digital signal processing and control and estimation theory
 Proceedings of the IEEE
, 1978
"... The purpose of this paper is to explore several current research directions in the fields of digital signal processing and modern control and estimation theory. We examine topics such as stability theory, linear prediction, and parameter identification, system synthesis and implementation, twodimens ..."
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Cited by 11 (4 self)
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The purpose of this paper is to explore several current research directions in the fields of digital signal processing and modern control and estimation theory. We examine topics such as stability theory, linear prediction, and parameter identification, system synthesis and implementation, twodimensional filtering, decentralized control and estimation, and image processing, in order to uncover some of the basic similarities and differences in the goals, techniques, and philosophy of the two disciplines.
PARAMETRIC MODELING OF ELECTROMAGNETIC WAVEFORMS
"... The problem of modeling and approximating a waveform by a linear combination of basis functions containing a variable parameter is considered. It is shown that the Kalman equation error concept of linear system identification theory can, in a modified form, be applied to a large class of modeling pr ..."
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Cited by 1 (0 self)
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The problem of modeling and approximating a waveform by a linear combination of basis functions containing a variable parameter is considered. It is shown that the Kalman equation error concept of linear system identification theory can, in a modified form, be applied to a large class of modeling problems, provided the chosen basis function is a solution of a linear functional equation in Hilbert space. This class includes rational and Tauberian modeling problems, known to be of relevance for electromagnetic transient response and wide bandwidth radar return signature identification respectively. 1
Modeling and Equalization of Audio Systems Using Kautz Filters
 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP’01
"... Kautz filters in a nutshell Kautz filters [6] are a class of fixedpole linearinparameter IIR filters � � ℄ , � � ℄ , composed of a transversal allpass backbone and allpole tapoutput filters, forced to produce orthonormal tapoutput impulse responses, and originating from rational orthonor ..."
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Cited by 1 (1 self)
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Kautz filters in a nutshell Kautz filters [6] are a class of fixedpole linearinparameter IIR filters � � ℄ , � � ℄ , composed of a transversal allpass backbone and allpole tapoutput filters, forced to produce orthonormal tapoutput impulse responses, and originating from rational orthonormal (basis) functions [10] defined by any set of points � � � �� thus determined by a set of (stable) poles � � � �� coefficients � � �: Some more or less familiar special cases: for � � it degenerates to an FIR filter
An interpretation of the auditory critical bands using a local Kautz transformation
 in Proc. ProRisc 8th anual workshop on Circuits, Systems and Signal Processing, Mierlo, The Netherlands
, 1997
"... The Zwicker data describes the relation between the critical bands and the center frequencies of the auditory system. The data can roughly be divided into two parts. At the low center frequencies the critical bandwidth is constant. At the high center frequencies the bandwidth increases in a monotoni ..."
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Cited by 1 (1 self)
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The Zwicker data describes the relation between the critical bands and the center frequencies of the auditory system. The data can roughly be divided into two parts. At the low center frequencies the critical bandwidth is constant. At the high center frequencies the bandwidth increases in a monotonic way with the center frequency. The following interpretation of this data is proposed. It is assumed that the auditory system performs a running orthogonal transformation, i.e., first the signal is windowed (localized in time) and next an orthogonal transformation is performed on the windowed signal. The constant bandwidth at the low center frequencies is interpreted as stemming from the (constant) window. The varying bandwidth at the high center frequencies can be modelled by a Kautz transformation. It is shown that such an interpretation holds not only qualitatively, but quantitatively as well. Possible applications are in the field of speech and audio coding since the model makes it poss...
Maximum Likelihood Estimation Of Exponentials Contained In SignalDependent Noises
, 1991
"... The problem of maximum likelihood estimation (MLE) of exponentials in signaldependent noise is addressed as well as a methodology to attack the problem. Estimation of exponentials has a long history, but much of the research is aimed at the stationary noise case. Signaldependent noise can arise ..."
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The problem of maximum likelihood estimation (MLE) of exponentials in signaldependent noise is addressed as well as a methodology to attack the problem. Estimation of exponentials has a long history, but much of the research is aimed at the stationary noise case. Signaldependent noise can arise in a variety of circumstances, however, e.g. magnetic and optical recording. A general signal model, loglikelihood function, and CramerRao lower bounds (CRB) are developed for the signaldependent noise case. A methodology splits the MLE problem into linear transformations, coarse search, localized search, and quality metric phases. An additional CRB derivation is used to assess the linear transforms for the estimation problem. Classical and multilayer perceptron artificial ...
Author manuscript, published in "N/P" Close shock detection using timefrequency Prony modeling
"... In many cases, modeling a mechanical process may require a good understanding of signals issued from the system, as vibration accelerations. This is particularly the case when shocks are responsible of the vibrations. In the case of critical systems, each shock induces natural modes excitation with ..."
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In many cases, modeling a mechanical process may require a good understanding of signals issued from the system, as vibration accelerations. This is particularly the case when shocks are responsible of the vibrations. In the case of critical systems, each shock induces natural modes excitation with damped sines amplitudes. Identification of the shocks