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560
On the Use of Windows for Harmonic Analysis With the Discrete Fourier Transform
 Proc. IEEE
, 1978
"... AhmwThis Pw!r mak = available a concise review of data win compromise consists of applying windows to the sampled daws pad the ^ affect On the Of in the data set, or equivalently, smoothing the spectral samples. '7 of aoise9 m the ptesence of sdroag bar The two operations to which we subject ..."
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Cited by 480 (0 self)
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AhmwThis Pw!r mak = available a concise review of data win compromise consists of applying windows to the sampled daws pad the ^ affect On the Of in the data set, or equivalently, smoothing the spectral samples. '7 of aoise9 m the ptesence of sdroag bar The two operations to which we subject the data are momc mterference. We dm call attention to a number of common = in be rp~crh of windows den used with the fd F ~ sampling and windowing. These operations can be performed transform. This paper includes a comprehensive catdog of data win in either order. Sampling is well understood, windowing is less related to sampled windows for DFT's. HERE IS MUCH signal processing devoted to detection and estimation. Detection is the task of determiningif a specific signal set is present in an observation, while estimation is the task of obtaining the values of the parameters
The Jackknife and the Bootstrap for General Stationary Observations
, 1989
"... this paper we will always consider statistics TN of the form TN (X 1 ; :::; XN ) = T (ae ..."
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Cited by 341 (2 self)
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this paper we will always consider statistics TN of the form TN (X 1 ; :::; XN ) = T (ae
Noise power spectral density estimation based on optimal smoothing and minimum statistics
 IEEE Trans. Speech and Audio Processing
, 2001
"... Abstract—We describe a method to estimate the power spectral density of nonstationary noise when a noisy speech signal is given. The method can be combined with any speech enhancement algorithm which requires a noise power spectral density estimate. In contrast to other methods, our approach does n ..."
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Cited by 242 (6 self)
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Abstract—We describe a method to estimate the power spectral density of nonstationary noise when a noisy speech signal is given. The method can be combined with any speech enhancement algorithm which requires a noise power spectral density estimate. In contrast to other methods, our approach does not use a voice activity detector. Instead it tracks spectral minima in each frequency band without any distinction between speech activity and speech pause. By minimizing a conditional mean square estimation error criterion in each time step we derive the optimal smoothing parameter for recursive smoothing of the power spectral density of the noisy speech signal. Based on the optimally smoothed power spectral density estimate and the analysis of the statistics of spectral minima an unbiased noise estimator is developed. The estimator is well suited for real time implementations. Furthermore, to improve the performance in nonstationary noise we introduce a method to speed up the tracking of the spectral minima. Finally, we evaluate the proposed method in the context of speech enhancement and low bit rate speech coding with various noise types. Index Terms—Minimum statistics, spectral estimation, speech enhancement. I.
Polynomial Splines and Their Tensor Products in Extended Linear Modeling
 Ann. Statist
, 1997
"... ANOVA type models are considered for a regression function or for the logarithm of a probability function, conditional probability function, density function, conditional density function, hazard function, conditional hazard function, or spectral density function. Polynomial splines are used to m ..."
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Cited by 189 (16 self)
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ANOVA type models are considered for a regression function or for the logarithm of a probability function, conditional probability function, density function, conditional density function, hazard function, conditional hazard function, or spectral density function. Polynomial splines are used to model the main effects, and their tensor products are used to model any interaction components that are included. In the special context of survival analysis, the baseline hazard function is modeled and nonproportionality is allowed. In general, the theory involves the L 2 rate of convergence for the fitted model and its components. The methodology involves least squares and maximum likelihood estimation, stepwise addition of basis functions using Rao statistics, stepwise deletion using Wald statistics, and model selection using BIC, crossvalidation or an independent test set. Publically available software, written in C and interfaced to S/SPLUS, is used to apply this methodology to...
Perspectives on system identification
 In Plenary talk at the proceedings of the 17th IFAC World Congress, Seoul, South Korea
, 2008
"... System identification is the art and science of building mathematical models of dynamic systems from observed inputoutput data. It can be seen as the interface between the real world of applications and the mathematical world of control theory and model abstractions. As such, it is an ubiquitous ne ..."
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Cited by 120 (3 self)
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System identification is the art and science of building mathematical models of dynamic systems from observed inputoutput data. It can be seen as the interface between the real world of applications and the mathematical world of control theory and model abstractions. As such, it is an ubiquitous necessity for successful applications. System identification is a very large topic, with different techniques that depend on the character of the models to be estimated: linear, nonlinear, hybrid, nonparametric etc. At the same time, the area can be characterized by a small number of leading principles, e.g. to look for sustainable descriptions by proper decisions in the triangle of model complexity, information contents in the data, and effective validation. The area has many facets and there are many approaches and methods. A tutorial or a survey in a few pages is not quite possible. Instead, this presentation aims at giving an overview of the “science ” side, i.e. basic principles and results and at pointing to open problem areas in the practical, “art”, side of how to approach and solve a real problem. 1.
Blind Separation of Mixture of Independent Sources Through a Maximum Likelihood Approach
 In Proc. EUSIPCO
, 1997
"... In this paper we propose two methods for separating mixtures of independent sources without any precise knowledge of their probability distribution. They are obtained by considering a maximum likelihood solution corresponding to some given distributions of the sources and relaxing this assumption af ..."
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Cited by 117 (8 self)
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In this paper we propose two methods for separating mixtures of independent sources without any precise knowledge of their probability distribution. They are obtained by considering a maximum likelihood solution corresponding to some given distributions of the sources and relaxing this assumption afterward. The first method is specially adapted to temporally independent non Gaussian sources and is based on the use of nonlinear separating functions. The second method is specially adapted to correlated sources with distinct spectra and is based on the use of linear separating filters. A theoretical analysis of the performance of the methods has been made. A simple procedure for choosing optimally the separating functions from a given linear space of functions is proposed. Further, in the second method, a simple implementation based on the simultaneous diagonalization of two symmetric matrices is provided. Finally, some numerical and simulation results are given illustrating the performan...
Source Separation Using Higher Order Moments
 in Proc. ICASSP
, 1989
"... This communication presents a simple algebraic method for the extraction of independent components in multidimensional data. Since statistical independence is a much stronger property than uncorrelation, it is possible, using higherorder moments, to identify source signatures in array data without ..."
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Cited by 110 (7 self)
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This communication presents a simple algebraic method for the extraction of independent components in multidimensional data. Since statistical independence is a much stronger property than uncorrelation, it is possible, using higherorder moments, to identify source signatures in array data without any apriori model for propagation or reception, that is, without directional vector parametrization, provided that the emitting sources be independent with different probability distributions. We propose such a "blind" identification procedure. Source signatures are directly identified as covariance eigenvectors after data have been orthonormalized and non linearily weighted. Potential applications to Array Processing are illustrated by a simulation consisting in a simultaneous rangebearing estimation with a passive array. INTRODUCTION For a lot of reasons (of various kinds), the most common Signal Processing methods deal with secondorder statistics, expressed in terms of covariance matr...
InformationTheoretic Analysis of Interscale and Intrascale Dependencies Between Image Wavelet Coefficients
 IEEE Transactions on Image Processing
, 2001
"... This paper presents an informationtheoretic analysis of statistical dependencies between image wavelet coefficients. The dependencies are measured using mutual information, which has a fundamental relationship to data compression, estimation, and classification performance. ..."
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Cited by 95 (1 self)
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This paper presents an informationtheoretic analysis of statistical dependencies between image wavelet coefficients. The dependencies are measured using mutual information, which has a fundamental relationship to data compression, estimation, and classification performance.
Frequency content of randomly scattered signals
 PART I, WAVE MOTION
, 1990
"... The statistical properties of acoustic signals reflected by a randomly layered medium are analyzed when a pulsed spherical wave issuing from a point source is incident upon it. The asymptotic analysis of stochastic equations and geometrical acoustics is used to arrive at a set of transport equatio ..."
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Cited by 89 (21 self)
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The statistical properties of acoustic signals reflected by a randomly layered medium are analyzed when a pulsed spherical wave issuing from a point source is incident upon it. The asymptotic analysis of stochastic equations and geometrical acoustics is used to arrive at a set of transport equations that characterize multiply scattered signals observed at the surface of the layered medium. The results of extensive numerical simulations are presented, illustrating the scope of the theory. A number of inverse problems for randomly layered media are also formulated where we
Bootstraps for Time Series
, 1999
"... We compare and review block, sieve and local bootstraps for time series and thereby illuminate theoretical facts as well as performance on nitesample data. Our (re) view is selective with the intention to get a new and fair picture about some particular aspects of bootstrapping time series. The ge ..."
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Cited by 89 (4 self)
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We compare and review block, sieve and local bootstraps for time series and thereby illuminate theoretical facts as well as performance on nitesample data. Our (re) view is selective with the intention to get a new and fair picture about some particular aspects of bootstrapping time series. The generality of the block bootstrap is contrasted by sieve bootstraps. We discuss implementational dis/advantages and argue that two types of sieves outperform the block method, each of them in its own important niche, namely linear and categorical processes, respectively. Local bootstraps, designed for nonparametric smoothing problems, are easy to use and implement but exhibit in some cases low performance. Key words and phrases. Autoregression, block bootstrap, categorical time series, context algorithm, double bootstrap, linear process, local bootstrap, Markov chain, sieve bootstrap, stationary process. 1 Introduction Bootstrapping can be viewed as simulating a statistic or statistical pro...