ICASSP99 Signal Modeling and Representation

Signal Modeling and Representation
Home Full List of Titles 1: Speech Processing CELP Coding Large Vocabulary Recognition Speech Analysis and Enhancement Acoustic Modeling I ASR Systems and Applications Topics in Speech Coding Speech Analysis Low Bit Rate Speech Coding I Robust Speech Recognition in Noisy Environments Speaker Recognition Acoustic Modeling II Speech Production and Synthesis Feature Extraction Robust Speech Recognition and Adaptation Low Bit Rate Speech Coding II Speech Understanding Language Modeling I 2: Speech Processing, Audio and Electroacoustics, and Neural Networks Acoustic Modeling III Lexical Issues/Search Speech Understanding and Systems Speech Analysis and Quantization Utterance Verification/Acoustic Modeling Language Modeling II Adaptation /Normalization Speech Enhancement Topics in Speaker and Language Recognition Echo Cancellation and Noise Control Coding Auditory Modeling, Hearing Aids and Applications of Signal Processing to Audio and Acoustics Spatial Audio Music Applications Application - Pattern Recognition & Speech Processing Theory & Neural Architecture Signal Separation Application - Image & Nonlinear Signal Processing 3: Signal Processing Theory & Methods I Filter Design and Structures Detection Wavelets Adaptive Filtering: Applications and Implementation Nonlinear Signals and Systems Time/Frequency and Time/Scale Analysis Signal Modeling and Representation Filterbank and Wavelet Applications Source and Signal Separation Filterbanks Emerging Applications and Fast Algorithms Frequency and Phase Estimation Spectral Analysis and Higher Order Statistics Signal Reconstruction Adaptive Filter Analysis Transforms and Statistical Estimation Markov and Bayesian Estimation and Classification 4: Signal Processing Theory & Methods II, Design and Implementation of Signal Processing Systems, Special Sessions, and Industry Technology Tracks System Identification, Equalization, and Noise Suppression Parameter Estimation Adaptive Filters: Algorithms and Performance DSP Development Tools VLSI Building Blocks DSP Architectures DSP System Design Education Recent Advances in Sampling Theory and Applications Steganography: Information Embedding, Digital Watermarking, and Data Hiding Speech Under Stress Physics-Based Signal Processing DSP Chips, Architectures and Implementations DSP Tools and Rapid Prototyping Communication Technologies Image and Video Technologies Automotive Applications / Industrial Signal Processing Speech and Audio Technologies Defense and Security Applications Biomedical Applications Voice and Media Processing Adaptive Interference Cancellation 5: Communications, Sensor Array and Multichannel Source Coding and Compression Compression and Modulation Channel Estimation and Equalization Blind Multiuser Communications Signal Processing for Communications I CDMA and Space-Time Processing Time-Varying Channels and Self-Recovering Receivers Signal Processing for Communications II Blind CDMA and Multi-Channel Equalization Multicarrier Communications Detection, Classification, Localization, and Tracking Radar and Sonar Signal Processing Array Processing: Direction Finding Array Processing Applications I Blind Identification, Separation, and Equalization Antenna Arrays for Communications Array Processing Applications II 6: Multimedia Signal Processing, Image and Multidimensional Signal Processing, Digital Signal Processing Education Multimedia Analysis and Retrieval Audio and Video Processing for Multimedia Applications Advanced Techniques in Multimedia Video Compression and Processing Image Coding Transform Techniques Restoration and Estimation Image Analysis Object Identification and Tracking Motion Estimation Medical Imaging Image and Multidimensional Signal Processing Applications I Segmentation Image and Multidimensional Signal Processing Applications II Facial Recognition and Analysis Digital Signal Processing Education Author Index A B C D E F G H I J K L M N O P Q R S T U V W X Y Z	An Optimal Generalized Theory of Signal Representation Authors: J. Scott Goldstein, Joseph R Guerci, Irving S Reed, Page (NA) Paper number 2207 Abstract: A new generalized statistical signal processing framework is introduced for optimal signal representation and compression. Previous work is extended by considering the multiple signal case, where a desired signal is observed only in the presence of other non-white signals. The solution to this multi-signal representation problem yields a generalization of the Karhunen-Loeve transform and generates a basis selection which is optimal for multiple signals and colored-noise random processes under the minimum mean-square error criterion. The important applications for which this model is valid include detection, prediction, estimation, compression, classification and recognition. IC992207.PDF (From Author) IC992207.PDF (Rasterized) TOP Symbolic Signal Processing Authors: Don H Johnson, Wei Wang, Page (NA) Paper number 1827 Abstract: Symbolic signals are, in discrete-time, sequences of quantities that do not assume numeric values. In the most general case, these quantities have no mathematical structure other than that they are members of some set, but they can have a sequential structure. We show that processing such signals does not entail mapping them directly to the integers, which would impose more structure---ordering and arithmetic---than present in the data. We describe how linear estimation and prediction can be performed on symbolic sequences. We show how spectrograms can be computed from neural population responses and from DNA sequences. IC991827.PDF (From Author) IC991827.PDF (Rasterized) TOP Using A New Uncertainty Measure To Determine Optimal Bases For Signal Representations Authors: Tomasz Przebinda, Victor DeBrunner, Murad Ozaydin, Page (NA) Paper number 1575 Abstract: We use a new uncertainty measure,Hp, that predicts the compactness of digital signal representations to determine a good (non-orthogonal) set of basis vectors. The measure uses the entropy of the signal and its Fourier transform in a manner that is similar to the use of the signal and its Fourier transform in the Heisenberg uncertainty principle. The measure explains why the level of discretization of continuous basis signals can be very important to the compactness of representation. Our use of the measure indicates that a mixture of (non-orthogonal) sinusoidal and impulsive or 'blocky' basis functions may be best for compactly representing signals. IC991575.PDF (From Author) IC991575.PDF (Rasterized) TOP Detection of Extra Solar Planets Using Parametric Modeling Authors: Andre Ferrari, Jean-Yves Tourneret, Francois-Xavier Schmider, Page (NA) Paper number 1307 Abstract: We present an algorithm for the detection of extra-solar planets by occultation on the satellite COROT. Under high flux assumption, the signal is modeled as an autoregressive process having equal mean and variance. A transit of a planet in front of a star will produce an abrupt jump in the mean/variance of the process. The Neyman-Pearson detector is derived when the abrupt change parameters are known. The theoretical distribution of the test statistic is obtained allowing the computation of the ROC curves. The generalized likelihood ratio detector is then studied for the practical case were the change parameters are unknown. This detector requires the maximum likelihood estimates of the parameters. ROC curves are then determined using computer simulations. IC991307.PDF (From Author) IC991307.PDF (Rasterized) TOP A DSB-SC Signal Model for Nonlinear Regression-Based Quadrature Receiver Calibration Authors: Roger A Green, Page (NA) Paper number 1405 Abstract: Recent advances have been made regarding quadrature receiver I/Q mismatch calibration. In particular, Green/Anderson-Sprecher/Pierre present a nonlinear regression (NLR) -based algorithm that utilizes a pure sinusoidal test signal for sensor calibration [1]. This paper develops a double side-band suppressed carrier (DSB-SC) signal model for use with NLR-based calibration methods. The DSB-SC model not only provides a useful signal for calibration, it also demonstrates the model flexibility inherent to nonlinear regression techniques. Simulations illustrate the effectiveness of the DSB-SC signal model for the calibration of I/Q sensors. IC991405.PDF (From Author) IC991405.PDF (Rasterized) TOP Model Selection: A Bootstrap Approach Authors: Abdelhak M Zoubir, Page (NA) Paper number 1853 Abstract: The problem of model selection is addressed. Bootstrap methods based on residuals are used to select the best model according to a prediction criterion. Both the linear and the nonlinear models are treated. It is shown that bootstrap methods are consistent and in simulations that in most cases they outperform classical techniques such as Akaike's information criterion and Rissanen's minimum description length. We also show how the methods apply to dependent data models such as autoregressive models. IC991853.PDF (From Author) IC991853.PDF (Rasterized) TOP Color Texture Synthesis with 2-D Moving Average Model Authors: Glen Andrews, Kie B Eom, Page (NA) Paper number 2069 Abstract: An algorithm for synthesizing color textures from a small set of parameters is presented in this paper. The synthesis algorithm is based on the 2-D moving average model, and realistic textures resembling many real textures can be synthesized using this algorithm. A maximum likelihood estimation algorithm to estimate parameters from a sample texture is also presented. Using the estimated parameters, a texture larger than the original image can be synthesized from a small texture sample. In the experiment, various textures suitable for multimedia applications are synthesized from parameters estimated from real textures. IC992069.PDF (From Author) IC992069.PDF (Rasterized) TOP Sampling Theorems for Linear Time-varying Systems with Bandlimited Inputs Authors: Soonman Kwon, Daniel R Fuhrmann, Page (NA) Paper number 2285 Abstract: We propose and prove an extended sampling theorem for linear time-varying systems. As a result, we establish a discrete-time equivalent model of the input-output relation of the system for the case of bandlimited inputs and bandlimited system variation. The sampling of the output signal and an equivalent discrete-time model of the system are discussed. IC992285.PDF (From Author) IC992285.PDF (Rasterized) TOP