We propose a new, robust pitch detection algorithm for speech degraded by additive noise. Our algorithm exploits the high correlation between adjacent pitch periods that does not exist for the segment as a whole and p...
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We propose a new, robust pitch detection algorithm for speech degraded by additive noise. Our algorithm exploits the high correlation between adjacent pitch periods that does not exist for the segment as a whole and performs well in the vicinity of voiced/unvoiced regions where the local SNR is low. The algorithm works as follows. We first determine an estimate of the pitch period near the short time peak energy where the local SNR is highest. We then adaptively estimate the local pitch period from the peak towards the transition region by using pitch synchronous cross-correlation with an updated waveform. The performance of this new algorithm is compared to the SIFT and CEPSTRUM algorithms.
A comprehensive analysis of the mean-square learning characteristics of stochastic-descent algorithms is presented. The approach is based on the commonly exploited simplifying assumption of stationary independent trai...
A comprehensive analysis of the mean-square learning characteristics of stochastic-descent algorithms is presented. The approach is based on the commonly exploited simplifying assumption of stationary independent training vectors. Characteristics analyzed include stability, steady-state misadjustment, initial rate of convergence, optimum step size, and steady-state autocovariance and spectral characteristics of the weight-vector. Effects on these characteristics due to degree of randomness of stochastic gradient, particular data distribution, and data corruption are isolated and analyzed. An objective of the work is to keep the number of simplifying assumptions and approximations to a minimum. Comparison of results with previous more approximate analyses are made. Lernkurven von stochastischen Gradienten Algorithmen werden untersucht. Die vereinfachende Annahme von stationären, unabhängigen Trainingsvektoren wird benutzt. Charakteristiken die untersucht werden beinhalten Stabilität, ‘steady state’ Fehlanpassung, Start Konvergenz, optimaler Schritt sowie ‘steady state’ Autokovarianz und spektrale Charakteristik des Gewichtsvektors. Die Effekten auf diese Charakteristiken von dem Zufälligkeitsgrad des stichastischen Gradienten, besonderer Daten Verteilung und Daten Verderbung werden isoliert und analysiert. Ein Zielpunkt dieser Arbeit ist es die Anzahl vereinfachenden Annahmen und Approximationen auf ein minimum zu beschränken. Vergleiche mit Resultaten die von approximativeren Analysen stammen werden gezogen. Une analyse d'ensemble des caractéristiques de convergence de l'erreur quadratique moyenne dans les algorithmes utilisant la décroissance du gradient stochastique est presentée. Cette approche est basée sur l'hypothése simplificatrice classique de stationnarité et indépendance des vecteurs de test. Les caractéristiques analysées comprennent la stabilité, l'écart d'état stable, la vitesse initiale de convergence, le pas optimum d'incrémentation, et l'autocovarian
A study aimed at segmenting a high-resolution black and white image of Sunnyvale, California, is described. In this study regions were classified as belonging to any one of nine classes: residential, commercial/indust...
A study aimed at segmenting a high-resolution black and white image of Sunnyvale, California, is described. In this study regions were classified as belonging to any one of nine classes: residential, commercial/industrial, mobile home, water, dry land, runway/taxiway, aircraft parking, multilane highway, and vehicle parking. The classes were selected so that they directly relate to the Defense Mapping Agency's Mapping, Charting and Geodesy tangible features. To attack the problem a statistical segmentation procedure was devised. The primitive operators used to drive the segmentation are texture measures derived from cooccurrence matrices. The segmentation procedure considers three kinds of regions at each level of the segmentation: uniform, boundary, and unspecified. At every level the procedure differentiates uniform regions from boundary and unspecified regions. In the assigns a class label to the uniform regions. The boundary and unspecified regions are split to form higher level regions. The methodologies involved are mathematically developed as a series of hypothesis tests. While only a one-level segmentation was performed studies are described which show the capabilities of each of these hypothesis tests. In particular an 83% correct classification was obtained in testing the labeling procedure. These studies indicate that the proposed procedure should be useful for land use classifications as well as other problems.
The well-known advantages of pipelining as applied to Finite Impulse Response (FIR) Residue Number System (RNS) arithmetic digital filters is extended to the important area of Infinite Impulse Response (IIR) digital f...
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The well-known advantages of pipelining as applied to Finite Impulse Response (FIR) Residue Number System (RNS) arithmetic digital filters is extended to the important area of Infinite Impulse Response (IIR) digital filters through a new technique based on augmentation of the IIR transfer function. Through this technique, pipelined IIR filters based on RNS Read-Only-Memory (ROM) table look-up techniques can be designed which offer throughput rates equal to the table look-up time of the ROM's. This high-speed realization can be achieved even though the recursive filter algorithm requires multiple delays in realizing the output of the filter. For the example of a typical second-order IIR filter, the pipelined structure represents a five-fold increase in speed over standard techniques. Higher order realizations will yield proportionately higher speed improvements. Although the new technique does increase somewhat the hardware complexity of the filter, the increase in speed will often justify the additional hardware. The paper discusses the basic technique, stability considerations, and hardware realizations.
Accurate and reliable detection of unique objects is an important component of an image understanding system. The objects are considered to have a unique pattern and should be recognized based upon their own character...
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Filtering audio signals with filters designed exclusively from frequency domain specifications may result in an audible distortion in the vicinity of sharp amplitude transitions. This paper considers the application o...
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Filtering audio signals with filters designed exclusively from frequency domain specifications may result in an audible distortion in the vicinity of sharp amplitude transitions. This paper considers the application of known psychoacoustical properties to the design of digital audio filters which minimizes this distortion while approximating some ideal frequency domain characteristics. Psychoacoustic properties and a simple model for hearing are reviewed. A weighted least squares design criteria based on the model and frequency domain specifications is given. Examples of FIR and IIR filters are given and compared to classical frequency domain filters.
In this paper we present some new results on Radon transform theory for stationary random fields. In particular we present a new projection theorem which gives the relation between the power spectrum density of one di...
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In this paper we present some new results on Radon transform theory for stationary random fields. In particular we present a new projection theorem which gives the relation between the power spectrum density of one dimensional projections of a stationary random field and its two dimensional power spectrum density. This result yields the optimum mean square reconstruction filter from noisy projections and is useful in other problems such as multidimensional spectral estimation from one dimensional projections, noise analysis in computed tomography, etc. Example are given to demonstrate the usefulness of these results.
The emphasis of many algorithms that have been proposed for the compression of binary images has been the efficient coding of local redundancy in data. We propose that increased compression may be achieved by a decomp...
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The emphasis of many algorithms that have been proposed for the compression of binary images has been the efficient coding of local redundancy in data. We propose that increased compression may be achieved by a decomposition of the compression problem into two steps. The goal of the first step is to extract the global redundancy in an image. This is achieved by a color shrinking algorithm, The goal of the second step is to code the resulting localized data.
作者：
BURT, PJImage Processing Laboratory
Electrical Computer and Systems Engineering Department Rensselaer Polytechnic Institute Troy New York 12181
A common task in image analysis is that of measuring image properties within local windows. Often usefulness of these property estimates is determined by characteristics of the windows themselves. Critical factors inc...
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A common task in image analysis is that of measuring image properties within local windows. Often usefulness of these property estimates is determined by characteristics of the windows themselves. Critical factors include the window size and shape, and the contribution the window makes to the cost of computation, A highly efficient procedure for computing property estimates within Gaussian-like windows is described. Estimates are obtained within windows of many sizes simultaneously.
A computer or microprocessor-based adaptive digital filter can easily be constructed to adapt structure as well as weights. These totally adaptive filters will always find a structure and set of weights which offer eq...
A computer or microprocessor-based adaptive digital filter can easily be constructed to adapt structure as well as weights. These totally adaptive filters will always find a structure and set of weights which offer equal or better error than the standard FIR adaptive filter yet avoid most of the difficulties encountered with IIR adaptive filters. computer simulations of the totally adaptive filter in a host of filtering applications confirm its superior performance when compared with any fixed-structure adaptive filter with an equal number of weights.
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