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IEEE International Conference on Acoustics, Speech, and signal processing

作者： Ouyang, S Bao, Z Guilin Univ Elect Technol Dept Commun & Informat Guilin 541004 Guangxi Peoples R China

ISBN: (纸本)0780370414

On the basis of the concepts of both weighted subspace criterion and information maximization, this paper proposes a weighted information criterion (WINC) for searching for the optimal solution of a homogeneous neural network. We develop two adaptive algorithms based on the WINC for extracting in parallel multiple principal components. The both algorithms are be able to provide an adaptive step size which leads to a significant improvement in the learning performance. Furthermore, the recursive least squares (RLS) version of WINC algorithms has a low computational complexity O(Np), where N is the input vector dimension and p is the number of desired principal components. Since the weighting matrix does not require an accurate value, it facilitates the system design of the WINC algorithm for real applications. Simulation results are provided to illustrate the effectiveness of WINC algorithms for PCA.

关键词： neural networks

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Several approaches to signal reconstruction from spectrum magnitudes

Several approaches to signal reconstruction from spectrum ma...

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IEEE International Conference on Acoustics, Speech, and signal processing

作者： Burian, A Saarinen, J Kuosmanen, P Rusu, C Tampere Univ Technol FIN-33101 Tampere Finland

ISBN: (纸本)0780370414

The problem of reconstructing a one-dimensional (1-D) signal from only the magnitude of its Fourier transform emerges when the phase of a signal is apparently lost or impractical to measure. Previous solutions usually employed an Iterative Fourier Transform (IFT) algorithm applied on a discrete approximation of a signal. The utilization of these algorithms is seriously limited by the unpredictability of their convergence. We propose several solutions to the phase retrieval problem. The first two proposed solutions uses relationships between the phase and the gain differences (GD), or gain samples (GS), in nepers. The last proposed solution uses a neural network (NN) for solving the problem. The NN incorporates a combination of the maximum entropy estimation algorithm with some additional nonlinear constraints. We compare our solutions by using some numerical examples. The performances under noisy conditions are also considered.

关键词： signal reconstruction

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New approaches to audio-visual segmentation of TV news for automatic topic retrieval

New approaches to audio-visual segmentation of TV news for a...

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IEEE International Conference on Acoustics, Speech, and signal processing

作者： Iurgel, U Meermeier, R Eickeler, S Rigoll, G Gerhard Mercator Univ Duisburg Dept Comp Sci Fac Elect Engn Duisburg Germany

ISBN: (纸本)0780370414

This paper presents two new real-time approaches to segmentation of TV news shows into topics. The goal of this research work is the high precision retrieval of topics from TV news. For that purpose, the detection of correct topic boundaries is of great importance. We introduce a stochastic and a rule-based topic model based on HMMs. The former combines features from the visual as well as from the audio channel of the news show, whereas the latter uses the video channel only. They are compared to the detection of topics using only the audio channel, which is common for many other approaches. The paper contains the following innovations: 1) The detected segment boundaries correspond directly to topics and not to video or audio cuts, as most other segmentation methods. 2) An advanced stochastic topic model is introduced that uses audio as well as video features. 3) The introduced HMM-based approaches both outperform the audio-based approach. One algorithm has a very good topic boundary detection rate, whereas the other minimizes the number of wrongly inserted boundaries without missing too many real boundaries.

关键词： Acoustic signal processing

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Adaptive transition bias for robust low complexity speech recognition

Adaptive transition bias for robust low complexity speech re...

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IEEE International Conference on Acoustics, Speech, and signal processing

作者： Koumpis, K Riis, SK Digital Signal Proc Grp Copenhagen Denmark

ISBN: (纸本)0780370414

The basis for all methods described in this paper is the application of an adaptive transition bias to the sequences of phoneme models that represent spoken utterances. This offers significantly improved accuracy in phoneme based speaker independent recognition, while adding very little overhead to the overall system complexity. The algorithms were tested using the low complexity hybrid recognizer denoted Hidden neural Networks (HNN) on US English and Japanese speaker independent name dialing tasks. Experimental results show that our approach provides a relative error rate reduction of up to 47% over the baseline system.

关键词： Speech recognition

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Sensor arrays in the micro-environment of the brain

Sensor arrays in the micro-environment of the brain

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IEEE International Conference on Acoustics, Speech, and signal processing

作者： Anderson, DJ Oweiss, KG Bierer, SM Univ Michigan Dept Elect Engn & Comp Sci Ann Arbor MI 48109 USA

ISBN: (纸本)0780370414

Technology for recording action potentials from the nervous system has changed in recent years from single or at most two or three neurons to tens of neurons recorded from geometrically precise arrays of recording sites. To fully exploit these research opportunities, signal delivery and processing are key factors. We desire not only sort the array input into neural channels but locate neurons with respect to the array so they can be later identified in histology and serve to further decode the activity of each neuron. The distribution of a neuron's signal across the sensor array is predictive of the cell location at least in projection onto the array but also in the dimension above the array. To achieve 3-dimensional locating power, careful estimation of signal strength at each site must be achieved taking into account the distance and the field distortion for all anticipated source positions. Achieving this independent of source strength appears to be feasible.

关键词： signal detection

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A stochastic analysis of the Affine Projection algorithm for Gaussian Autoregressive inputs

A stochastic analysis of the Affine Projection algorithm for...

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IEEE International Conference on Acoustics, Speech, and signal processing

作者： Bershad, NJ Linebarger, D McLaughlin, S Univ Calif Irvine Dept Elect & Comp Engn Irvine CA 92697 USA

ISBN: (纸本)0780370414

This paper studies the statistical behavior of the Affine Projection (AP) algorithm for mu = 1 for Gaussian Autoregressive inputs. This work extends the theoretical results of Rupp [3] to the numerical evaluation of the MSE learning curves for the adaptive AP weights. The MSE learning behavior of the AP(P+ 1) algorithm with an AR(Q) input (Q less than or equal to P) is shown to be the same as the NLMS algorithm (mu = 1) with a white input with M-P unity eigenvalues and P zero eigenvalues and increased observation noise. Monte Carlo simulations are presented which support the theoretical results.

关键词： Adaptive algorithms

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A new technique for blind source separation using subband subspace analysis in correlated multichannel signal environments

A new technique for blind source separation using subband su...

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IEEE International Conference on Acoustics, Speech, and signal processing

作者： Oweiss, KG Anderson, DJ Elec. Eng. and Comp. Sci. Dept. University of Michigan Ann arbor MI 48109 United States

ISBN: (纸本)0780370414

We investigate a new framework for the problem of blind source identification in multichannel signal processing. Inspired by a neurophysiological data environment, where an array of closely spaced recording electrodes is surrounded by multiple neural cell sources [1], significant spatial correlation of source signals motivated the need for an efficient technique for reliable multichannel blind source identification. In a previous work [2], we adopted a new approach for noise suppression based on thresholding an Array Discrete Wavelet Transform (ADWT) representation of the multichannel data. We extent the work in [2] to identify sources from the observation mixtures. The technique relies on separating sources with highest spatial energy distribution in each frequency subband spanned by the corresponding wavelet basis. Accordingly, the best basis selection criterion we propose benefits from the additional degree of freedom offered by the space domain. The amplitude and shift invariance properties revealed by this technique make it very efficient to track spatial source variations sometimes encountered in multichannel neural recordings. Results from multichannel multiunit neural data are presented and the overall performance is evaluated.

关键词： Blind source separation

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Kalman Filtering and neural Networks

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丛书名： Adaptive and learning systems for signal processing, communications, and control

2001年

作者： Simon Haykin

ISBN: (数字)9780471221548

ISBN: (纸本)9780471369981

State-of-the-art coverage of Kalman filter methods for the design of neural networks This self-contained book consists of seven chapters by expert contributors that discuss Kalman filtering as applied to the training and use of neural networks. Although the traditional approach to the subject is almost always linear, this book recognizes and deals with the fact that real problems are most often nonlinear. The first chapter offers an introductory treatment of Kalman filters with an emphasis on basic Kalman filter theory, Rauch-Tung-Striebel smoother, and the extended Kalman filter. Other chapters cover:* An algorithm for the training of feedforward and recurrent multilayered perceptrons, based on the decoupled extended Kalman filter (DEKF)* Applications of the DEKF learning algorithm to the study of image sequences and the dynamic reconstruction of chaotic processes* The dual estimation problem* stochastic nonlinear dynamics: the expectation-maximization (EM) algorithm and the extended Kalman smoothing (EKS) algorithm* The unscented Kalman filter Each chapter, with the exception of the introduction, includes illustrative applications of the learning algorithms described here, some of which involve the use of simulated and real-life data. Kalman Filtering and neural Networks serves as an expert resource for researchers in neural networks and nonlinear dynamical systems. An Instructor's Manual presenting detailed solutions to all the problems in the book is available upon request from the Wiley Makerting Department.

关键词： Kalman filtering. neural networks (Computer science)

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一种基于CMAC的图象恢复算法

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中国图象图形学报（A辑） 2001年第8期6卷 780-783页

作者：赵保军史彩成沈胜宏韩月秋北京理工大学电子工程系北京100081

由于影响成象和导致图象退化的因素具有模糊性和不确定性 ,很难准确地建立图象退化过程的数学模型 ,因而建立退化过程的逆过程图象恢复十分困难 .为了解决这一问题 ,提出了一种基于 CMC的图象恢复算法 ,该方法利用 CMAC神经网络的非线... 详细信息

由于影响成象和导致图象退化的因素具有模糊性和不确定性 ,很难准确地建立图象退化过程的数学模型 ,因而建立退化过程的逆过程图象恢复十分困难 .为了解决这一问题 ,提出了一种基于 CMC的图象恢复算法 ,该方法利用 CMAC神经网络的非线性映射和综合能力 ,通过对影响成象和导致图象退化的过程进行反向学习来恢复图象 .仿真结果表明 ,用 CMAC神经网络能很好地恢复出已退化的图象 ,并且神经网络模型与学习方法十分简单 ,便于实时图象恢复 .

关键词：图象恢复 CMAC神经网络图象退化图象处理

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Nonlinear Biomedical signal processing 1

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丛书名： IEEE Press Series on Biomedical Engineering) (Volume 2

2000年

作者： Akay, Metin

ISBN: (数字)9780470545379

ISBN: (纸本)9780780360129

Featuring current contributions by experts in signal processing and biomedical engineering, this book introduces the concepts, recent advances, and implementations of nonlinear dynamic analysis methods. Together with Volume I in this series, this book provides comprehensive coverage of nonlinear signal and image processing techniques. "Nonlinear Biomedical signal processing: Volume ii" combines analytical and biological expertise in the original mathematical simulation and modeling of physiological systems. Detailed discussions of the analysis of steady-state and dynamic systems, discrete-time system theory, and discrete modeling of continuous-time systems are provided. Biomedical examples include the analysis of the respiratory control system, the dynamics of cardiac muscle and the cardiorespiratory function, and neural firing patterns in auditory and vision systems. Examples include relevant MATLAB(R) and Pascal programs. Topics covered include: LI Nonlinear dynamics LI Behavior and estimation LI Modeling of biomedical signals and systems LI Heart rate variability measures, models, and signal assessments LI Origin of chaos in cardiovascular and gastric myoelectrical activity LI Measurement of spatio-temporal dynamics of human epileptic seizures A valuable reference book for medical researchers, medical faculty, and advanced graduate students, it is also essential reading for practicing biomedical engineers. "Nonlinear Biomedical signal processing, Volume ii" is an excellent companion to Dr. Akay's "Nonlinear Biomedical signal processing, Volume I: Fuzzy Logic, neural Networks, and New Algorithms."

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