A novel approach is presented for characterizing transient wandering tones. These signals are segmented and approximated as time series with piecewise linear instantaneous frequency and piecewise constant amplitude. F...
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A novel approach is presented for characterizing transient wandering tones. These signals are segmented and approximated as time series with piecewise linear instantaneous frequency and piecewise constant amplitude. Frequency rate, center frequency, and energy features are estimated in each segment of data using chirped autocorrelations and the fractional Fourier transform. These features are tracked across segments using linear dynamical models whose parameters are estimated using an expectation-maximization algorithm. A new cross-covariance estimator for adjacent states of the dynamical model is given. The feature extraction/tracking algorithm is used to characterize a measured marine-mammal vocalization. Application of the representation algorithm to signal classification is discussed.
A fuzzy c-means approach to the expectation-maximisation (EM) algorithm is proposed. A family of fuzzy EM algorithms of various degrees of fuzziness is presented, where the EM algorithm is referred to as a fuzzy EM al...
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A fuzzy c-means approach to the expectation-maximisation (EM) algorithm is proposed. A family of fuzzy EM algorithms of various degrees of fuzziness is presented, where the EM algorithm is referred to as a fuzzy EM algorithm with degree of fuzziness of one. This fuzzy approach can be applied to EM-style algorithms such as the Baum-Welch algorithm for hidden Markov models, and the EM algorithm for Gaussian mixture models in speech and speaker recognition. The fuzzy EM algorithm for Gaussian mixture models is considered in detail as a demonstration for applying the fuzzy EM algorithm.
We propose an optimum channel estimation algorithm for DS-CDMA systems. This algorithm performs an iterative estimation of the channel according to the maximum a posteriori criterion, using the expectation-maximizatio...
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We propose an optimum channel estimation algorithm for DS-CDMA systems. This algorithm performs an iterative estimation of the channel according to the maximum a posteriori criterion, using the expectation-maximization algorithm. It requires a convenient representation of the discrete multipath fading channel based on the Karhunen-Loeve orthogonal expansion theorem.
This paper discusses some theoretical results on underdetermined source separation, i.e. when the mixing matrix is degenerate, especially when there is more sources than observations. In this case, we show that the so...
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This paper discusses some theoretical results on underdetermined source separation, i.e. when the mixing matrix is degenerate, especially when there is more sources than observations. In this case, we show that the sources can be restored up to an arbitrary additive random vector. In the particular case of discrete sources, very relevant for digital communications, we show that this vector is certain.
In this paper, a computationally efficient, easily implementable algorithm for MAP restoration of images degraded by blur and additive correlated Gaussian noise using Gibbs prior density functions is derived, This alg...
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In this paper, a computationally efficient, easily implementable algorithm for MAP restoration of images degraded by blur and additive correlated Gaussian noise using Gibbs prior density functions is derived, This algorithm is valid for a variety of complete data spaces, The constraints upon the complete data space arising from the Gaussian image formation model are analyzed and a motivation is provided for the choice of the complete data, based upon the ease of computation of the resulting EM algorithms, The overlooked role of the null space of the blur operator in image restoration is introduced, An examination of this role reveals an important drawback to the use of the simulated annealing algorithm in maximizing a specific class of functionals, An alternative iterative method for computing the nullspace component of a vector is given, The ability of a simple Gibbs prior density function to enable partial recovery of the component of an image within the nullspace of the blur operator is demonstrated.
We propose a modified radial basis function (RBF) network in which the regression weights are used to replace the constant weights in the output layer, It is shown that the modified RBF network can reduce the number o...
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We propose a modified radial basis function (RBF) network in which the regression weights are used to replace the constant weights in the output layer, It is shown that the modified RBF network can reduce the number of hidden units significantly. A computationally efficient algorithm, known as the expectation-maximization (EM) algorithm, is used to estimate the parameters of the regression weights, A salient feature of this algorithm is that it decomposes a complicated multiparameter optimization problem into L separate small-scale optimization problems, where L is the number of hidden units, The superior performance of the modified RBF network over the standard RBF network is illustrated by computer simulations.
Statistical neural networks executing soft decision algorithms have been shown to be very effective in many classification problems. A neural network architecture is developed here that can perform unsupervised joint ...
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Statistical neural networks executing soft decision algorithms have been shown to be very effective in many classification problems. A neural network architecture is developed here that can perform unsupervised joint segmentation and labeling of objects in images. We propose the semi-parametric hierarchical mixture density (HMD) model as a tool for capturing the diversity of real world images and pose the object recognition problem as a maximum likelihood (ML) estimation of the HMD parameters. We apply the expectation-maximization (EM) algorithm for this purpose and utilize ideas and techniques from statistical physics to cast the problem as the minimization of a free energy function. We then proceed to regularize the solution thus obtained by adding smoothing terms to the objective function. The resulting recursive scheme for estimating the posterior probabilities of an object's presence in an image corresponds to an unsupervised feedback neural network architecture. We present here the results of experiments involving recognition of traffic signs in natural scenes using this technique.
It has been demonstrated that object recognition can be formulated as an image-restoration problem. In this approach, which we term impulse restoration, the objective is to restore a delta function indicating the obje...
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It has been demonstrated that object recognition can be formulated as an image-restoration problem. In this approach, which we term impulse restoration, the objective is to restore a delta function indicating the object's location. We develop solutions based on impulse restoration for the Gaussian noise case. We propose a new iterative approach, based on the expectation-maximization (EM) algorithm, that simultaneously estimates the background statistics and restores a delta function at the location of the template. We use localization-receiver-operating characteristics (LROC) curves to evaluate quantitatively the performance of this approach and compare it with existing methods. We present experimental results that demonstrate that impulse restoration is a powerful approach for detecting known objects in images severely degraded by noise. Our experiments demonstrate that accurate modeling and estimation of the background and noise statistics are essential to successful object detection.
Modeling acoustic correlation in automatic speech recognition systems is essential when the speech signal is non stationary or corrupted by noise. We present a statistical method for improved acoustic modeling in cont...
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Modeling acoustic correlation in automatic speech recognition systems is essential when the speech signal is non stationary or corrupted by noise. We present a statistical method for improved acoustic modeling in continuous density hidden Markov models (HMMs). Factor analysis uses a small number of parameters to model the covariance structure of the speech signal. These parameters are estimated by an expectation-maximization algorithm, then further adjusted using discriminative minimum classification error training. Experimental results on 1219 New Jersey town names demonstrate that the proposed method produces faster, smaller and more accurate recognition models.
The authors quantitatively and qualitatively examine the use of a Gibbs prior in maximum a posteriori (MAP) reconstruction of SPECT images of pulmonary perfusion using the expectation-maximization algorithm (EM). This...
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The authors quantitatively and qualitatively examine the use of a Gibbs prior in maximum a posteriori (MAP) reconstruction of SPECT images of pulmonary perfusion using the expectation-maximization algorithm (EM). This Bayesian approach is applied to SPECT projection data acquired from a realistic torso phantom with spherical defects in the lungs simulating perfusion deficits. Both the scatter subtraction constant (k) and the smoothing parameter beta (/spl beta/) characterizing the prior are varied to study their affect on image quality and quantification. Region of interest (ROI) analysis is used to compare MAP-EM radionuclide concentration estimates with those derived from a "clinical" implementation of filtered backprojection (CFBP), and a quantitative implementation of FBP (QFBP) utilizing nonuniform attenuation and scatter compensation. Qualitatively, the MAP-EM images contain reduced artifacts near the lung boundaries relative to the FBP implementations. Overall MAP-FM image visual quality and the ability to discern the areas of reduced radionuclide concentration in the lungs depend on the value of /spl beta/ and the total number of iterations. For certain choices of /spl beta/ and total iterations, MAP-EM lung images are visually comparable to FBP. Based on profile and ROI analysis, SPECT QFBP and MAP-FM images have the potential to provide quantitatively accurate reconstructions when compared to CFBP; computational burden, however, is greater for the MAP-FM approach. To demonstrate the potential clinical efficacy of the methods, the authors present pulmonary perfusion images of a patient with lung cancer.
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