Various types of statistical signal processing require the estimation of time-variant correlation functions and the fitting of time-variant models for nonstationary processes. In this paper, the situations for which n...
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Various types of statistical signal processing require the estimation of time-variant correlation functions and the fitting of time-variant models for nonstationary processes. In this paper, the situations for which nonstationary probabilistic correlation functions can be accurately estimated from a single sample path of a stochastic process are delineated. These include only (i) known form of nonstationarity, (ii) periodic or almost periodic nonstationarity, and (iii) slowly fluctuating nonstationarity. Known methods of estimation for each of these situations are reviewed within the unifying framework of orthogonal series expansions. The fact that estimators based on orthogonal series expansions for nonstationary correlation functions other than (i)–(iii) cannot be guaranteed to be accurate is established. Ramifications for time-variant autoregressive model fitting are discussed. Verschiedene Aufgabenbereiche der Signalverarbeitung erfordern die Schätzung zeitvarianter Korrelationsfunktionen und die zeitvariante Modellierung nichtstationärer Prozesse. Im folgenden werden die Situationen dargelegt, für die nicht-stationäre Korrelationsfunktionen aus einem einzigen Repräsentanten eines Prozesses geschätzt werden können. Erfaßt werden können nur Fälle mit (i) bekannter Form der Instationarität, (ii) periodischer oder fast-periodischer Instationarität und (iii) langsam veränderlicher Instationarität. Die bekannten Schätzverfahren für jede dieser Gegebenheiten werden in einem einheitlichen Rahmen, dem der Orthogonalreihen-Entwicklung, zusammengestellt. Die Grenzen der Nutzung von Reihenentwicklungen bei der Schätzung nicht-stationärer Korrelationsfunktionen werden aufgezeigt. Variationsmöglichkeiten für die zeitvariante AR-Modellierung werden diskutiert. De nombreuses operations de traitement du signal requièrent l'estimation de fonctions de corrélation dependent du temps et la construction de modèles variant avec le temps pour les processus nonstationnaires. Dans cet a
The three-dimensional Schrödinger equation inverse scattering problem with a nonspherically-symmetric potential is related to the filtering problem of computing the linear leastsquares estimate of the three-dimen...
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A digital lossless bounded-real (LBR) transfer function, more commonly known as a stable allpass function, maintains the LBR property under multiplier coefficient quantization when implemented by networks that are str...
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A digital lossless bounded-real (LBR) transfer function, more commonly known as a stable allpass function, maintains the LBR property under multiplier coefficient quantization when implemented by networks that are structurally LBR. A systematic method is outlined for generating all possible such realizations for second-order transfer functions containing a minimum number of multipliers and delays, allowing the designer to take advantage of the flexibility offered by a catalog of such realizations. Illustrative examples are included.
Experimental analyses of an implementation of an SIMD algorithm for recursive digital filtering using the partitionable SIMD/MIMD (PASM) parallelprocessing system prototype at Purdue University are presented. The alg...
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Experimental analyses of an implementation of an SIMD algorithm for recursive digital filtering using the partitionable SIMD/MIMD (PASM) parallelprocessing system prototype at Purdue University are presented. The algorithm used easily generalizes to use N processing elements (PEs). Timing-based analyses are made based on a four-PE version by examining the following constituent execution times: microcontroller execution time, PE execution time, broadcast communication time, and the execution time of five additional phases in the recursive digital filtering summation calculation. Broadcast execution time was found to account for roughly 44% of the total execution time, and the implication of this is discussed for larger problem sizes and machine sizes. The total measured execution time is verified through summation of execution times for the various components of the algorithm.< >
Two interrelated personal computer (PC) based speech training aids have been developed: one for use in a school or clinic, the Speech Training Station (STS);and the other for the deaf child's home, the Speech Prac...
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Two interrelated personal computer (PC) based speech training aids have been developed: one for use in a school or clinic, the Speech Training Station (STS);and the other for the deaf child's home, the Speech Practice Station (SPS). The STS monitors speech production by microphone, electroglottograph, and pneumotachograph. The SPS system uses only the microphone input. Both systems utilize commercially available board-level hardware and a custom analog preprocessor board for the analysis of the acoustic and/or physiologic inputs. The school system has been used by speech therapists for diagnosis, training by game playing, and specification of exercises for the SPS. The home system provides directed speech practice between therapy sessions.
A speech model with an excitation that is a multipulse sequence with additive white noise is presented. This model, when used in a 4.8-kb/s communication system, has the potential for improved synthetic speech quality...
A speech model with an excitation that is a multipulse sequence with additive white noise is presented. This model, when used in a 4.8-kb/s communication system, has the potential for improved synthetic speech quality. The algorithm is described and convergence for synthetic data of the assumed structure is proved. A method for significantly improved efficiency is presented, based on an algorithm given by M. Korenberg (1985). Initial experimental data that support both the authors' and Korenberg algorithms are given.< >
An approach to time-domain pitch detection based on the concepts of center of mass is presented and evaluated. Excursions, or bumps, in speech signals are treated as geometric areas and replaced by their total mass lu...
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An approach to time-domain pitch detection based on the concepts of center of mass is presented and evaluated. Excursions, or bumps, in speech signals are treated as geometric areas and replaced by their total mass lumped at the centers of mass. Intervals between masses are grouped into candidate classes. Coincidence and coherence indices of these classes are computed to determine the most likely pitch estimate. Postprocessing consists of a simple error-correction and silence-detection scheme. This algorithm compares favourably in performance with the autocorrelation method, using pitch contours from electroglottograph signals as a reference. The algorithm is tested in noisy environments simulated by uniformly distributed white noise and multitalker babble noise. Results show that the algorithm is robust and accurate. The implementation of this algorithm for a vibrotactile device to aid lipreading is described.< >
In ocean acoustic tomography, maximal length binary shift- register sequences, m-sequences, are used to modulate acoustic carriers to achieve high average power and good time and Doppler resolution. To date, the under...
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In ocean acoustic tomography, maximal length binary shift- register sequences, m-sequences, are used to modulate acoustic carriers to achieve high average power and good time and Doppler resolution. To date, the underwater transmitters and receivers have been in fixed positions, and signal processing has consisted of demodulation followed by factor inverse filtering. Ocean tomography now is being extended to include the use of moving, ship-towed, transmitters and receivers, where signal processing must account for Doppler time and frequency resealing. This paper describes the signal demodulation and processing methods developed for moving ship tomography and presents illustrative results.
A new deflected stochastic-gradient algorithm for adaptive sensor arrays is introduced. It is shown to have the capability of converging substantially faster than the nondeflected stochastic gradient (LMS) algorithm b...
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A new deflected stochastic-gradient algorithm for adaptive sensor arrays is introduced. It is shown to have the capability of converging substantially faster than the nondeflected stochastic gradient (LMS) algorithm but slower than the recursive least squares (RLS) algorithm. Because of its amenability to reduction of computational complexity by data quantization, it has the potential for processing digital signals with bandwidths one to two orders of magnitude larger than the bandwidths manageable with the RLS algorithm for sensor arrays containing a number of adjustable weights in the approximate range of 10-50.
The importance of the concept of cyclostationarity in design and analysis of signal detectors, synchronizers, and extractors in communication systems is briefly discussed, and the central role of spectral correlation,...
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The importance of the concept of cyclostationarity in design and analysis of signal detectors, synchronizers, and extractors in communication systems is briefly discussed, and the central role of spectral correlation, in the characterization of random processes that are cyclostationary in the wide sense, is explained. A spectral correlation function that is a generalization of the power spectral density function is described, and a corresponding generalization of the Wiener-Khinchine relation and several other fundamental spectral correlation relations also are described. Explicit formulas for the spectral correlation function for various types of analog-modulated signals are derived. This includes pulse and carrier amplitude modulation, quadrature amplitude carrier modulation, and phase and frequency carrier modulation. To illustrate the differing spectral correlation characteristics of different modulation types, the magnitudes of the spectral correlation functions are graphed or described in graphical terms as the heights of surfaces above a bifrequency plane.
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