ICASSP99 System Identification, Equalization, and Noise Suppression

System Identification, Equalization, and Noise Suppression
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	A Robust M-estimate Adaptive Filter For Impulse Noise Suppression Authors: Yuexian Zou, Department of Electrical and Electronic Engineering, The University of Hong Kong (Hong Kong) S.C. Chan, Department of Electrical and Electronic Engineering, The University of Hong Kong (Hong Kong) T.S. Ng, Department of Electrical and Electronic Engineering, The University of Hong Kong (Hong Kong) Page (NA) Paper number 1948 Abstract: In this paper, a robust M-estimate adaptive filter for impulse noise suppression is proposed. The objective function used is based on a robust M-estimate. It has the ability to ignore or down weight large signal error when certain thresholds are exceeded. A systematic method for estimating such thresholds for adaptation is also proposed. An advantage of the proposed method is that its solution is governed by a system of linear equation. Therefore, fast adaptation algorithms for traditional linear adaptive filters can be applied. In particular, a M-estimate recursive least square (M-RLS) adaptive algorithm is studied in detail. Simulation results show that it is more robust against individual and consecutive impulse noise than the MN-LMS [2] and the N-RLS algorithms [7]. It also has fast convergence speed and a low steady state error similar to its RLS counterpart. IC991948.PDF (From Author) IC991948.PDF (Rasterized) TOP Local Adaptive De-Noising Techniques In Transform Domain For EMCG De-Noising Authors: Hakan Öktem, Tampere University of Technology Signal Processing Laboratory Finland (Finland) Karen O Egiazarian, Tampere University of Technology Signal Processing Laboratory Finland (Finland) Juha Nousiainen, Tampere University of Technology Ragnar Granit Institute Finland (Finland) Page (NA) Paper number 1676 Abstract: There are various de-noising algorithms and optimization methods for different signal and noise characteristics. However, the signals used in real application may have deviations from the model. For example: signal and/or noise may not be stationary or a proper model for them may not be available. MCG (magnetocardiography) is an example signal, where conventional de-noising methods are not giving satisfactory results. Local adaptive processing allow to modify filtering parameters according to the specific properties of different 'portions' of a signal. In this paper a methodology for adopting the transform domain local adaptive processing to the specific task of MCG signal de-noising is introduced. IC991676.PDF (From Author) IC991676.PDF (Rasterized) TOP A Stochastic Diffusion Approach to Signal Denoising Authors: Hamid Krim, Yufang Bao, ECE Dept., NCSU, Raleigh NC 27695-7914 and BUPT, P.R. China (China) Page (NA) Paper number 2195 Abstract: We present a stochastic formulation of a linear diffusion equation (or heat equation), and in light of the potential applications ranging from signal denoising to image enhancement/segmentation of its nonlinear extensions, we propose a more general nonlinear stochastic diffusion. The constructed stochastic framework, in contrast to traditional deterministic approaches, unveils the sources of of existing limitations and allows us to further significantly improve the performance by addressing the key problem. Substantiating examples are provided. IC992195.PDF (From Author) IC992195.PDF (Rasterized) TOP Amplitude Estimation with Application to System Identification Authors: Petre Stoica, Systems and Control Group, Uppsala University, Uppsala, Sweden. (Sweden) Hongbin Li, Department of Electrical and Computer Engineering,University of Florida, Gainesville, Florida, USA. (USA) Jian Li, Department of Electrical and Computer Engineering,University of Florida, Gainesville, Florida, USA. (USA) Page (NA) Paper number 1016 Abstract: We investigate herein the problem of amplitude estimation of sinusoidal signals from observations corrupted by colored noise. A relatively large number of amplitude estimators are described which encompass Least Squares (LS) and Weighted Least Squares (WLS) methods. Additionally, filterbank approaches, which are widely used for spectral analysis, are extended to amplitude estimation. Specifically, we consider the recently introduced MAtched-FIlterbank (MAFI) approach and show that, by appropriately designing the prefilters, the MAFI approach includes the WLS approach. The amplitude estimation techniques discussed in this paper do not model the noise, and yet they are all asymptotically statistically efficient. It is their different finite-sample properties that are of particular interest to this study. Numerical examples are provided to illustrate the differences among the various estimators. Though amplitude estimation applications are numerous, we focus on system identification using sinusoidal probing signals. IC991016.PDF (From Author) IC991016.PDF (Rasterized) TOP Performance Analysis of Two Approaches to Closed Loop System Identification via Cyclic Spectral Analysis Authors: Channarong Tontiruttananon, Jitendra K Tugnait, Page (NA) Paper number 1321 Abstract: The problem of closed loop system identification given noisy time-domain input-output measurements is considered. It is assumed that the various disturbances affecting the system are zero-mean stationary whereas the closed loop system operates under an external cyclostationary input which is not measured. Noisy measurements of the (direct) input and output of the plant are assumed to be available. The closed loop system must be stable but it is allowed to be unstable in open loop. Recently we proposed two identification algorithms using cyclic-spectral analysis of noisy input-output data. In this paper we provide an asymptotic performance analysis of the recently proposed parameter estimators. Computer simulation examples are presented in support of the analysis. IC991321.PDF (From Author) IC991321.PDF (Rasterized) TOP Blind Channel Identification Using RLS Method Based on Second-Order Statistics Authors: Takashi Kimura, Hideaki Sasaki, Hiroshi Ochi, Faculty of Eng., Univ. of the Ryukyus (U.K.) Page (NA) Paper number 1409 Abstract: In this paper, we show a new blind identification algorithm which is based on second order statistics and exploits a Single-Input Double-Output(SIDO) model. It is suitable for a real-time processing system because of lower operation and high-speed convergence. The proposed blind identification algorithm is superior to conventional algorithms in view of simple structure and the uniqueness of solution. We also verify its efficiency by computer simulation. IC991409.PDF (From Author) IC991409.PDF (Rasterized) TOP A Deterministic Blind Identification Technique for SIMO Systems of Unknown Model Order Authors: Gopal T Venkatesan, Lang Tong, Mostafa Kaveh, Ahmed H Tewfik, Kevin M Buckley, Page (NA) Paper number 1040 Abstract: In this paper we present a method for the deterministic blind identification of single-input multiple-output systems with unknown model order. The technique, that is applicable to both the FIR and IIR cases, requires only an upper bound of the model order. It is based on the special kernel structure of block Toeplitz matrices. When the model order is overestimated, this special structure entails the true solution to be embedded in the overestimated solution in a unique shift-chain form. This special shift-chain structure is then utilized to extract the true solution. IC991040.PDF (From Author) IC991040.PDF (Rasterized) TOP Blind Channel Equalization Using Weighted Subspace Methods Authors: Rafael Ruiz, Margarita Cabrera, Page (NA) Paper number 1251 Abstract: This paper addresses the problems of blind channel estimation and symbol detection with second order statistics methods from the received data. It can be shown that this problem is similar to Direction Of Arrival (DOA) estimation, where many solutions like the MUSIC algorithm or "weighted" techniques (as Deterministic Maximum Likelihood or Weighted Subspace Fitting method) have been developed. In this proposal we extend these techniques to blind channel identification problem in an unified framework known as Subspace Fitting. In this framework the estimated and the received data are "fitting" through the subspaces in a least square sense. Then, in order to solve this problem and estimate the channel, a modified Gauss-Newton type algorithm is suggested. Simulations are carried out comparing the proposed solutions with a classical signal subspace-based blind channel identification scheme. IC991251.PDF (From Author) IC991251.PDF (Rasterized) TOP A Least Squares Component Normalization Approach to Blind Channel Identification Authors: Carlos Avendano, Jacob Benesty, Dennis R Morgan, Page (NA) Paper number 1741 Abstract: We describe a new method for blind system identification that uses the cross relation properties between two or more sensor signals to estimate the impulse responses of the channels. The method performs as well or better than other similar blind identification techniques under noisy and ill-conditioned channel conditions, and is computationally simpler to implement. IC991741.PDF (From Author) IC991741.PDF (Rasterized) TOP Blind Equalization of Multiuser CDMA Channels: a Frequency-Domain Approach Authors: Konstantinos I Diamantaras, Dept. of Informatics, Technological Education Institute, GR-54101 Sindos, Greece (Greece) Athina P Petropulu, Page (NA) Paper number 2407 Abstract: The blind estimation of mixing channels resulting from frequency selective fading and multipath in a multi-user CDMA system is an important problem in wireless communications. We present a novel frequency-domain approach using second order spectral statistics for recovering the unknown channels. Unlike other methods which are based on time-domain analysis we make no particular assumption about the support of the mixing channels except that they have finite length (FIR). The method is based on the fact that the source sequences obtain a known spectral color derived from the corresponding spreading code used in CDMA. IC992407.PDF (Scanned) TOP