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Neural Networks for Signal Processing IX, 1999. Proceedings of the 1999 IEEE Signal Processing Society Workshop.

Date 25-25 Aug. 1999

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  • Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468)

    Publication Year: 1999
    Request permission for commercial reuse | PDF file iconPDF (218 KB)
    Freely Available from IEEE
  • Author index

    Publication Year: 1999, Page(s):565 - 566
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    Freely Available from IEEE
  • A suboptimal Bayesian equalizer using an nonlinear multilayer combiner

    Publication Year: 1999, Page(s):353 - 362
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (468 KB)

    In order to reduce the complexity and enhance the performance of the Bayesian equalizer (REQ) using the radial basis function (RBF) network, a new equalizer (RNEQ) using the RBF network with a nonlinear multilayer combiner is proposed. The proposed RNEQ produces the output using nonlinear multilayer combiner. The RNEQ is applied to a digital communication system and a nonlinear magnetic storage sy... View full abstract»

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  • A comparative study of a hidden Markov model detector for atrial fibrillation

    Publication Year: 1999, Page(s):468 - 476
    Cited by:  Papers (4)  |  Patents (6)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (372 KB)

    A comparative study of several atrial fibrillation (AF) detection algorithms was done to determine the algorithm best suited for use in real clinical environments to detect AF in ambulatory ECGs. The algorithms that were investigated for this paper are based on the Hidden Markov Model (HMM), measures of variance, linear predictive coding, and measurement of approximate entropy (AE). Based on the r... View full abstract»

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  • Maximum likelihood blind source separation in Gaussian noise

    Publication Year: 1999, Page(s):343 - 352
    Cited by:  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (396 KB)

    This paper presents a new maximum likelihood (ML) approach to the separation of convolutive mixtures of unobserved sources in the presence of additive temporally white Gaussian noise (ATWGN). The proposed method proceeds in two steps. First, the ML estimate of the mixing system is computed and, afterwards, this estimate is employed to obtain the ML estimates of the sources. The proposed algorithms... View full abstract»

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  • Multi-channel piecewise selective averaging of cognitive evoked potentials with variable latency

    Publication Year: 1999, Page(s):459 - 467
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (520 KB)

    This work is about the development of an alternative way of averaging evoked potentials (EP) of cognitive activities. Since the main assumption of invariant waveforms time locked to the eliciting events does not hold for cognitive EPs, averaging results in distorted estimates. The authors' alternative selective averaging finds similar subsequences of fixed length with variable latency which are co... View full abstract»

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  • Neuro Bayesian blind equalization with BER estimation in digital channels

    Publication Year: 1999, Page(s):333 - 342
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (508 KB)

    The implementation of an optimal Bayesian algorithm for digital equalization is infeasible due to its computational complexity. We present a new approach to Bayesian blind equalization which is based on a hybrid architecture involving neural networks and evolutionary computation concepts. We develop a theoretical analysis which leads to recursive formulas to estimate the probability density functi... View full abstract»

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  • Subspace techniques in speech enhancement

    Publication Year: 1999, Page(s):449 - 458
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (456 KB)

    This paper concerns speech enhancement. Speech is first modelled and then model parameters used to design a Kalman smoother to reduce the background noise. The 4SID (subspace state space system identification), DOA (direction-of-arrival) and polynomial techniques are described within a common subspace state space framework, and assumptions for each technique stated. It is shown that 4SID methods m... View full abstract»

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  • Separation of acoustic signals using self-organizing neural networks

    Publication Year: 1999, Page(s):324 - 332
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (396 KB)

    Spectral modeling is an essential component in many signal processing applications, such as speech enhancement and sound monitoring. This paper demonstrates its use in the separation of acoustic sources from a compound signal that is registered by one sensor. Our technique distinguishes itself from the popular blind source separation procedure by its much higher noise insensitivity and its ability... View full abstract»

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  • Why a nonlinear solution for a linear problem? [channel equalization]

    Publication Year: 1999, Page(s):157 - 165
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (440 KB)

    We emphasize a key point that when there is noise in the system, even if the system is linear, a nonlinear solution is more desirable. We derive a simple expression that shows that for a linear regression model, the logistic nonlinearity will be the natural match for modeling posterior class probabilities, and that the steepness of this logistic function is inversely proportional to the level of n... View full abstract»

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  • Model combination and weight selection criteria for speaker verification

    Publication Year: 1999, Page(s):439 - 448
    Cited by:  Papers (1)  |  Patents (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (428 KB)

    We focus on the score combination for three separate modeling approaches as applied to text-dependent speaker verification. The modeling methods that are evaluated consist of the neural tree network (NTN), hidden Markov model (HMM), and dynamic time warping (DTW). One of the main challenges in combining scores of several models is how to select the weight for each model. One method is to use equal... View full abstract»

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  • Global stability in delayed cellular neural networks

    Publication Year: 1999, Page(s):95 - 102
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (280 KB)

    The problem of global asymptotic stability of a class of delayed cellular neural networks (DCNNs) is further addressed by means of the Lyapunov functional method and the inequality a3+b3+c3⩾3abc(a,b,c⩾0) techniques, a set of criteria are found ensuring the global asymptotic stability of DCNNs with more general output functions. These criteria have important... View full abstract»

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  • Allpass vs. unit-norm constraints in contrast-based blind deconvolution

    Publication Year: 1999, Page(s):273 - 282
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (416 KB)

    Many contrast-based independent component analysis (ICA) algorithms can be directly applied to single-channel blind deconvolution tasks without modification. For prewhitened signals, however, an allpass constraint on the filter impulse response is more appropriate than a unit-norm constraint. We show how a finite-impulse-response filter can be adapted to maximize or minimize an arbitrary contrast ... View full abstract»

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  • Fisher discriminant analysis with kernels

    Publication Year: 1999, Page(s):41 - 48
    Cited by:  Papers (558)  |  Patents (18)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (464 KB)

    A non-linear classification technique based on Fisher's discriminant is proposed. The main ingredient is the kernel trick which allows the efficient computation of Fisher discriminant in feature space. The linear classification in feature space corresponds to a (powerful) non-linear decision function in input space. Large scale simulations demonstrate the competitiveness of our approach View full abstract»

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  • Clustering approach to square and non-square blind source separation

    Publication Year: 1999, Page(s):315 - 323
    Cited by:  Papers (8)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (380 KB)

    Recently, a number of heuristic techniques, mostly based on topographic maps, have been introduced in order to overcome (some) of the limitations of the blind source separation (BSS) algorithms that are rooted in the theory of independent component analysis. Here, we introduce a new heuristic that relies on the tendency of the mixture samples to cluster around the source directions in mixture spac... View full abstract»

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  • Partial likelihood methods for probability density estimation

    Publication Year: 1999, Page(s):147 - 156
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (428 KB)

    Partial likelihood (PL) establishes a sufficiently general framework to develop and study statistical properties of nonlinear techniques in signal processing. Adah et al. (1997), present the theorem by which the fundamental information-theoretic relationship for learning the PL cost, the equivalence of likelihood maximisation and relative entropy minimization, is established. In this paper, we ref... View full abstract»

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  • General statistical inference by an approximate application of the maximum entropy principle

    Publication Year: 1999, Page(s):112 - 121
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (492 KB)

    We propose a learning method for building a general statistical inference engine, operating on discrete feature spaces. Such a model allows inference on any feature given values for the other features (or for a feature subset). Bayesian networks (BNs) are versatile tools that possess this inference capability. However, while the BN's explicit representation of conditional independencies is informa... View full abstract»

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  • A topology independent active contour tracking

    Publication Year: 1999, Page(s):429 - 438
    Cited by:  Papers (3)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (576 KB)

    In previous years, the active contour (snake) has become one of the most powerful segmentation algorithms in image processing and computer vision. However, most algorithms based on this model have difficulties in automatic initialization and are hard to handle the problems with topology changes or multiple-objects tracking. We propose a model algorithm, quad-tree highest confidence first (QHCF), f... View full abstract»

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  • Gradient based adaptive regularization

    Publication Year: 1999, Page(s):87 - 94
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (288 KB)

    A technique to optimize regularization parameters for a given supervised training problem is presented. A training database is applied to minimize a regularized cost function, and a validation database is used to estimate and optimize generalization properties by means of a modification of regularization. The performance is validated for a vowel classification task and compared to other approaches View full abstract»

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  • An adaptive metric learning procedure for reconfigurable facial signature authentication

    Publication Year: 1999, Page(s):409 - 418
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (380 KB)

    We present an adaptive metric learning procedure with improved generalization of missing training data for facial signature recognition for use in a smart card system. The conventional learning models suffer from degraded recognition rate due to poor estimation of the margin of a decision boundary. Our model employs an image synthesis method to represent missing patterns of unknown classes by usin... View full abstract»

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  • Neural architectures for parametric estimation of a posteriori probabilities by constrained conditional density functions

    Publication Year: 1999, Page(s):263 - 272
    Cited by:  Papers (10)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (384 KB)

    A new approach to the estimation of `a posteriori' class probabilities using neural networks, the Joint Network and Data Density Estimation (JNDDE), is presented. It is based on the estimation of the conditional data density functions, with some restrictions imposed by the classifier structure; the Bayes' rule is used to obtain the `a posteriori' probabilities from these densities. The proposed me... View full abstract»

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  • Sequential support vector machines

    Publication Year: 1999, Page(s):31 - 40
    Cited by:  Papers (9)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (420 KB)

    We derive an algorithm to train support vector machines sequentially. The algorithm makes use of the Kalman filter and is optimal in a minimum variance framework. It extends the support vector machine paradigm to applications involving real-time and non-stationary signal processing. It also provides a computationally efficient alternative to the problem of quadratic optimisation View full abstract»

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  • Hierarchy of probabilistic principal component subspaces for data mining

    Publication Year: 1999, Page(s):497 - 506
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (612 KB)

    Visual exploration has proven to be a powerful tool for multivariate data mining. Most visualization algorithms aim to find a projection from the data space down to a visually perceivable rendering space. To reveal all of the interesting aspects of complex data sets living in a high-dimensional space, a hierarchical visualization algorithm is introduced which allows the complete data set to be vis... View full abstract»

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  • Geometrical structures of FIR manifold and their application to multichannel blind deconvolution

    Publication Year: 1999, Page(s):303 - 312
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (424 KB)

    We study geometrical structures on the manifold of FIR filters and their application to multichannel blind deconvolution. First we introduce the Lie group and Riemannian metric to the manifold of FIR filters. Then we derive the natural gradient on the manifold using the isometry of the Riemannian metric. Using the natural gradient, we present a novel learning algorithm for blind deconvolution base... View full abstract»

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  • Robust machine fault detection with independent component analysis and support vector data description

    Publication Year: 1999, Page(s):67 - 76
    Cited by:  Papers (11)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (428 KB)

    We propose an approach to fault detection in rotating mechanical machines: fusion of multichannel measurements of machine vibration using independent component analysis (ICA), followed by a description of the admissible domain (part of the feature space indicative of normal machine operation) with a support vector domain description (SVDD) method. The SVDD method enables the determination of an ar... View full abstract»

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