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SIMULTANEOUS ESTIMATION OF POLES AND ZEROS IN SPEECH ANALYSIS AND ITIF-ITERATIVE INVERSE filtering ALGORITHM

IEEE TRANSACTIONS ON ACOUSTICS SPEECH AND SIGNAL PROCESSING 1979年第5期27卷 485-492页

作者： KONVALINKA, IS MATAUSEK, MR UNIV BELGRADE DEPT ELECT ENGN BELGRADE YUGOSLAVIA

A new algorithm is proposed for the simultaneous estimation of poles and zeros in speech analysis. The algorithm is based on estimating the unknown system input and improvement of this estimate through the iterations. Thus, the algorithm does not require any type of preliminary deconvolution of the speech waveform, such as synchronization with pitch period or homomorphic deconvolution. Detailed analysis of a simulated system, as well as a preliminary analysis of initial nasal consonants /m/ and /n/, are presented. These analyses have shown that the ITIF algorithm gives a very accurate fit of the spectra of the systems analyzed. The iterative inverse filtering algorithm (ITIF) is a new technique for modeling linear systems having unknown input with a flat spectral envelope, such as pulse train or white noise input, by applying the pole-zero model. The ITIF algorithm in each iteration solves two linear parameter estimation problems: in the first one the unknown system input is estimated; in the second one the estimated input is used to determine the parameters of the pole-zero model. Experiments made so far have shown that the algorithm converges in a small number of iterations.

关键词： Poles and zeros Speech analysis filtering algorithms Deconvolution Algorithm design and analysis Iterative algorithms Analytical models Linear systems White noise Parameter estimation

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MULTICHANNEL ARMA MODELING BY LEAST-SQUARES CIRCULAR LATTICE filtering

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 1994年第9期42卷 2304-2318页

作者： CHAKRABORTY, M PRASAD, S INDIAN INST TECHNOL DEPT ELECTR & ELECT COMMUN ENGNKHARAGPUR 721302W BENGALINDIA

This paper makes an attempt to develop least squares lattice algorithms for the ARMA modeling of a linear, slowly time-varying, multichannel system employing scalar computations only. Using an equivalent scalar, periodic ARMA model and a circular delay operator, the signal set for each channel is defined in terms of circularly delayed input and output vectors corresponding to that channel. The orthogonal projection of each current output vector on the subspace spanned by the corresponding signal set is then computed in a manner that allows independent AR and MA order recursions. The resulting lattice algorithm can be implemented in a parallel architecture employing one processor per channel with the data flowing amongst them in a circular manner. The evaluation of the ARMA parameters from the lattice coefficients follows the usual step-up algorithmic approach but requires, in addition, the circulation of certain variables across the processors since the signal sets become linearly dependent beyond certain stages. The proposed algorithm can also be used to estimate a process from two correlated, multichannel processes adaptively allowing the filter orders for both the processes to be chosen independently of each other. This feature is further exploited for ARMA modeling a given multichannel time series with unknown, white input.

关键词： Least squares methods Lattices filtering algorithms Time varying systems Delay Vectors Adaptive filters Nonlinear filters Parallel architectures Signal processing

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ADAPTIVE DISTRIBUTED filtering IN MULTICOORDINATED SYSTEMS

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 1991年第4期27卷 715-724页

作者： LANG, H Department of Electrical Engineering Wright State University Dayton OH USA

When the statistics of the system modeling error, the measurement errors, and the communication network uncertainties are unknown, the distributed Kalman filtering in the multicoordinated systems becomes suboptimal or nonoptimal depending upon the prior information about the unknown statistics. An algorithm for estimation of the optimal steady state gains of distributed filtering and adaptive distributed filtering in the multi-coordinated systems is developed using the correlation method. The algorithm starts with the prior information about the unknown statistics, and adaptively adjusts the weights for the best integration of the multiple measurement sequences.

关键词： Adaptive filters Sensor phenomena and characterization Sensor systems Aerodynamics Covariance matrix Communication networks filtering algorithms Statistical distributions Measurement errors Information filtering

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SHORT-LENGTH FIR FILTERS AND THEIR USE IN FAST NONRECURSIVE filtering

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 1991年第6期39卷 1322-1332页

作者： MOU, ZJ DUHAMEL, P NATL RES CTR TELECOMMUN PABRPEF-92131 ISSY MOULINEAUXFRANCE

This paper provides the basic tools required for an efficient use of the recently proposed fast FIR algorithms. These algorithms not only reduce arithmetic complexity but also partially maintain the multiply-accumulate structure, thus resulting in efficient implementations. A set of basic algorithms is derived, together with some rules for combining them. Their efficiency is compared with that of classical schemes in the case of three different criteria, corresponding to various types of implementation. It is shown that this class of algorithms (which includes classical ones as special cases) makes it possible to find the best tradeoff corresponding to any criterion.

关键词： Finite impulse response filter Signal processing algorithms filtering algorithms Arithmetic Hardware Convolution Pipeline processing Digital signal processors Telecommunications Very large scale integration

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Efficient 2D Tikhonov smoothness regularization with recursive filtering

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PATTERN RECOGNITION LETTERS 2023年 175卷 95-103页

作者： Ferreira, Hermes H. Gastal, Eduardo S. L. Univ Fed Rio Grande do Sul Inst Informat Porto Alegre RS Brazil

In this work, we describe an implementation of the 2D Tikhonov regularization filter which scales lin-early with the input signal's size. In the homogeneous case, we propose a novel algorithm to decompose the filter's 2D kernel as a sum of axis-aligned Gaussians. Our algorithm uses symmetries of the kernel to provide a fast computation of the Gaussian decomposition in the frequency domain, where the 2D Tikhonov kernel has a closed-form expression. The convolution with each Gaussian is then computed using linear-time separable recursive filtering. In the non-homogeneous case, we also decompose the 2D problem as a series of iterated linear-time separable recursive filters, which can be combined with the Bi-conjugate Gradient Stabilized method for fast convergence. In this way, a fast solution to the 2D Tikhonov regularization problem is obtained. (c) 2023 Elsevier B.V. All rights reserved.

关键词： Image processing filtering Convolution Frequency -domain analysis filtering algorithms

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GSP-KalmanNet: Tracking Graph Signals via Neural-Aided Kalman filtering

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 3700-3716页

作者： Buchnik, Itay Sagi, Guy Leinwand, Nimrod Loya, Yuval Shlezinger, Nir Routtenberg, Tirza Beng Gurion Univ Negev Sch Elect & Comp Engn ECE IL-84105 Beer Sheva Israel

Dynamic systems of graph signals are encountered in various applications, including social networks, power grids, and transportation. While such systems can often be described as state space (SS) models, tracking graph signals via conventional tools based on the Kalman filter (KF) and its variants is typically challenging. This is due to the nonlinearity, high dimensionality, irregularity of the domain, and complex modeling associated with real-world dynamic systems of graph signals. In this work, we study the tracking of graph signals using a hybrid model-based/data-driven approach. We develop the GSP-KalmanNet, which tracks the hidden graphical states from the graphical measurements by jointly leveraging graph signal processing (GSP) tools and deep learning (DL) techniques. The derivations of the GSP-KalmanNet are based on extending the KF to exploit the inherent graph structure via designing a graph frequency domain filtering and replacing the Kalman gain (KG) with a graph filter that minimizes the prediction error. Thus, it considerably simplifies the computational complexity entailed in processing high-dimensional signals and increases the robustness to small topology changes. Then, we use data to learn the KG, namely, the graph filter, following the recently proposed KalmanNet framework, which copes with partial and approximated modeling, without forcing a specific model over the noise statistics. Restricting the KG to a graph filter in the proposed GSP-KalmanNet reduces learned parameters, thereby enhancing stability. Our empirical results demonstrate that the GSP-KalmanNet achieves enhanced accuracy and run time performance, and improved robustness to model misspecifications compared with both model-based and data-driven benchmarks.

关键词： Complexity theory Computational modeling Kalman filters Frequency-domain analysis filtering algorithms Numerical models Heuristic algorithms Graph signal processing (GSP) graph filters deep learning Kalman filtering

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Multidimensional Blind Deconvolution Method Based on Cross-Sparse filtering for Weak Fault Diagnosis

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IEEE ACCESS 2020年 8卷 209415-209427页

作者： Wang, Shan Zhang, Zongzhen Wang, Jinrui Han, Baokun Shandong Univ Sci & Technol Coll Mech & Elect Engn Qingdao 266000 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Energy & Power Engn Nanjing 210016 Peoples R China

Fault diagnosis and condition monitoring of rotating machinery has drawn considerable attention. The complex structure of rotating machinery and poor working conditions cause two challenges: weak signature detection (WSD) and weak compound fault separation (WCFS). A superior method should realize these two functions simultaneously. This paper proposed a multidimensional blind deconvolution method based on cross-sparse filtering (Cr-SF) for WSD and WCFS, which can enhance the weak signature and decompose the different components from compound fault adaptively without any preprocessing and priori knowledge. Cross kurtosis pursuit (CKP), a novel filter selection technology, is proposed for determining the final filters. The experimental and simulated signals verified the performance of the proposed algorithm. The robustness is also investigated using the success rate of repeated experiments. The results indicate that Cr-SF can handle different fault compounding modes under the strong noise environment and perform strong robustness and noise adaptability.

关键词： Fault diagnosis Handwriting recognition filtering algorithms Robustness Compounds Machinery Optimization Unsupervised learning cross-sparse filtering blind deconvolution weak signature detection compound fault separation

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BLOCK Z-TRANSFORM AND ITS APPLICATION TO FIR filtering

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 1991年第10期39卷 2335-2343页

作者： LI, WP PETERSON, AM STANFORD UNIV DEPT ELECT ENGNSTANFORDCA 94305

The block Z transform (BZT) is presented. It is shown that the BZT, used with the modified Fermat number transform (MFNT), is very efficient for FIR filtering of a long impulse response. The BZT takes advantage of the number theoretic transforms (NTT's), namely, the computational efficiency, and overcomes one of the restrictions on the NTT's, namely, the restriction on the length of the impulse response.

关键词： Finite impulse response filter Convolution Nonlinear filters Computational efficiency Signal processing Computer science Signal processing algorithms filtering algorithms Sections

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FAST FINITE-DIFFERENCE TIME-DOMAIN ANALYSIS OF RESONATORS USING DIGITAL filtering AND SPECTRUM ESTIMATION TECHNIQUES

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IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES 1992年第8期40卷 1611-1619页

作者： BI, ZQ SHEN, Y WU, KL Communications Research Laboratory McMaster University Hamilton ONT Canada

In this paper we discuss the use of digital filtering and spectrum estimation techniques for improving the efficiency of the FD-TD algorithm in solving eigenvalue problems. The great improvement of the efficiency of the method is demonstrated by means of both numerical and measurement results. In addition, several improvements to the present FD-TD method for eigenvalue analysis are presented. These include the analysis of open dielectric resonators and the extraction of the resonant frequencies from the FD-TD results. The result for the open dielectric resonator analysis is validated using measured data.

关键词： Finite difference methods Time domain analysis Eigenvalues and eigenfunctions Dielectric measurements Digital filters Resonator filters Spectral analysis filtering algorithms Data mining Resonant frequency

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Distributed Multiple Model filtering for Markov Jump Systems With Measurement Outliers

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2023年第3期59卷 2823-2837页

作者： Li, Hui Yan, Liping Xia, Yuanqing Beijing Inst Technol Sch Automat Key Lab Intelligent Control Decis Complex Syst Beijing 100081 Peoples R China

In this article, a distributed filtering problem is studied for a Markov jump system over sensor networks, where measurements are partially disturbed by outliers. A local multiple model filter is designed based on variational Bayesian approaches and interacting multiple model methods, the designed filter is able to identify and exclude outliers automatically, so as to mitigate the impact of outliers. A distributed filter is proposed by combining the designed local filter with consensus on information methods. Furthermore, a sufficient condition is given to guarantee the stability of the designed distributed filter, in which the estimation errors of each sensor are bounded in the mean square sense. Finally, both simulations and experiments of target tracking systems are done to show the effectiveness of the designed distributed filter.

关键词： Information filters Markov processes Estimation Robot sensing systems Loss measurement Bayes methods filtering algorithms Distributed filter Markov jump systems (MJSs) outliers sensor networks stability

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