Modified Autocorrelation Method Research Articles

Predicting Tipping Points in Chaotic Maps with Period-Doubling Bifurcations

In this paper, bifurcation points of two chaotic maps are studied: symmetric sine map and Gaussian map. Investigating the properties of these maps shows that they have a variety of dynamical solutions by changing the bifurcation parameter. Sine map has symmetry with respect to the origin, which causes multistability in its dynamics. The systems’ bifurcation diagrams show various dynamics and bifurcation points. Predicting bifurcation points of dynamical systems is vital. Any bifurcation can cause a huge wanted/unwanted change in the states of a system. Thus, their predictions are essential in order to be prepared for the changes. Here, the systems’ bifurcations are studied using three indicators of critical slowing down: modified autocorrelation method, modified variance method, and Lyapunov exponent. The results present the efficiency of these indicators in predicting bifurcation points.

Pitch estimation based on instantaneous robust algorithm for pitch tracking using weighted linear prediction-based complex speech analysis

Pitch estimation plays an important role on speech processing such as speech coding, synthesis, recognition, and so on. Although current pitch estimation method performs well under clean condition, the performance deteriorates significantly in noisy environment. For this reason, robust pitch estimation against additive noise is required. Modified auto-correlation method is commonly used as pitch estimation in which LP residual is used to compute the auto-correlation. We have previously proposed pitch estimation methods based on Time-Varying Complex AR (TV-CAR) analysis whose criterion is the weighted correlation of the complex residual obtained by the TV-CAR analysis, sum of the harmonics for the complex residual spectrum, or so on. On the other hand, Azarov et al. have proposed an improved method of RAPT (Robust Algorithm for Pitch Tracking) using an instantaneous harmonics that is called IRAPT (Instantaneous RAPT). The IRAPT can perform better estimation than RAPT. Since IRAPT uses band-limited analytic signal to obtain harmonic frequencies, the complex residual signal obtained by the TV-CAR analysis can also be applied to the IRAPT. In this paper, novel pitch estimation method using the instantaneous frequency based on the robust WLP (Weighted Linear Prediction) TV-CAR residual is proposed and evaluated.

Statistical Approaches to Estimating the Relative Contribution of Intermolecular Interactions in Aliphatic Alcohols: Application to QSPR/QSAR Modeling of Their Boiling Points

A three-layer feed forward neural network trained with a Levenberg–Marquardt batch error back propagation algorithm has been used to model the strong relationships between the boiling point of aliphatic alcohols and intermolecular forces consisting both in Van Der Waals forces and polar interactions, respectively. For that purpose, we use the multifunctional autocorrelation method to provide an appropriate topological description. Two types of descriptors are generated: the first is commonly used in QSARs and QSPRs modelling, it gives a general description of the whole of the molecule; the second is attributed to the local description of the group hydroxyl. In this we have turned our interests to the explanatory capacities of our methodology to explore the relative contribution and/or the contribution profile of the input factors compared to the size of the molecule. The initial data set is divided into different subsets in increasing order of values of boiling point. Then, we explore the good descriptive ability of the molecular descriptors calculated solely from the modified autocorrelation method to carry out a variable analysis and give information about features of the compounds responsible for their boiling points. This is made possible by comparing the regression coefficients for the established linear model, and by using the Garson weight portioning method for the ANN analysis.

Ultrasound-based vessel wall tracking: an auto-correlation technique with RF center frequency estimation

Vessel diameter is related to the distending blood pressure, and is used in estimations of vessel stiffness parameters. The vessel walls can be tracked by integrating wall velocities estimated by ultrasound (US) Doppler techniques. The purpose of this work was to evaluate the performance of the modified autocorrelation estimator when applied on vessel wall motion. As opposed to the conventional autocorrelation method that only estimates the mean Doppler frequency, the modified autocorrelation method estimates both the mean Doppler frequency and the radiofrequency (RF) center frequency. To make a systematic evaluation of the estimator, we performed computer simulations of vessel wall motion, where pulse bandwidth, signal-to-noise ratio (SNR), signal-to-reverberation ratio, packet size and sample volume were varied. As reference, we also analyzed the conventional autocorrelation method and the cross-correlation method with parabolic interpolation. Under the simulation conditions considered here, the modified autocorrelation method had the lowest bias and variance of the estimators. When integrating velocity estimates over several cardiac cycles, the resulting tissue displacement curves might drift. This drift is directly related to the magnitude of the estimator bias and variance. Hence, the modified autocorrelation method should be the preferred choice of method. (E-mail: s.i.rabben@klinmed.uio.no)

Utilisation de la méthode d'autocorrélation modifiée pour étudier des relations structure–déplacement chimique 13C pour des alcanes

Abstract Use of a modified autocorrelation method to study 13C chemical shifts-chemical structure relationships for alkanes. The concept of the multifunctional autocorrelation method governing global description of molecules was modified, in order to generate new atomic environments. Such atomic environments allow the generation of atomic descriptors by means of a modified autocorrelation method. The principle of this approach is explained through a case study dealing with the design of a model allowing the simulation of the carbon-13 nuclear magnetic spectra for alkanes. The data set contains 243 values of chemical shifts. Carbon atoms in alkanes are described by using as structural descriptor a vector corresponding to only four components of the multifunctional autocorrelation method. The established model allows us the prediction of the 13C chemical shift with high success. Results obtained by means of multiple regression analysis was less satisfying than those obtained using neural network approach. The quality of the neural network model was evaluated by means of the Cross validation technique, with a mean error between δexp and δcalc of 0.60 ppm.

Hybrid Pitch Extractor for Speech Utterances

This paper presents a new pitch detection algorithm similar to the Semiautomatic Pitch Detector (SAPD) [1] in the sense that it initially calculates the pitch period with a time-domain principle cycle extractor. However, it differs from SAPD in that the pitch periods are not only calculated on time-domain, but are assured with an error detector. If there is any ambiguity, the pitch period will be re-calculated with the CEP algorithm. A 'pitch selection logic' is used to select the pitch period from the two estimates. Experimental results show that the algorithm exhibits good performance in the five measurements proposed in [2]. The new algorithm runs at half the speed of the Data Reduction Method (DARD) [3], but faster than the Modified Autocorrelation Method (AUTOC) [4] and the Cepstrum Method [5] in the order of four and eight times, respectively.

Autocorrelation modeling of lipophilicity with a back-propagation neural network

From a training set of 7200 chemicals a back-propagation neural network (BNN) model was developed for estimating the n-octanol/water partition coefficient of organic molecules. Chemicals were described by means of a modified autocorrelation method. The advantages of the autocorrelation method were emphasized through the analysis of the simulation performances of the model and from a comparative study involving another BNN model [Quant. Struct. Act. Relat. 16 (1997) 224–230] using a large number of variables (atoms and bonds) derived from connection matrices.

Prediction of Partition Coefficients (LOG P oct) Using Autocorrelation Descriptors

A backpropagation neural network model, implemented in AUTOLOGP (Version 4.0), was developed for estimating the n-octanol/water partition coefficient of organic molecules from their structure described by means of a modified autocorrelation method. The advantages of the autocorrelation method, which allows the description of any kind of molecules by means of computerized molecular descriptors presenting a physicochemical meaning, were emphasized through the simulation performances obtained with AUTOLOGP (Version 4.0) and from a comparative study involving two regression models derived from various topological indices.

A General QSAR Model for Predicting the Toxicity of Organic Chemicals to Luminescent Bacteria (Microtox® test)

Abstract A large data set of Microtox® toxicity results was used to derive a general QSAR model. Chemicals were described by means of a modified autocorrelation method. The autocorrelation vectors were generated from atomic contributions encoding the hydrophobicity and molar refractivity of the molecules. A three-layer backpropagation neural network was used to design the model. The obtained results were compared with those obtained from a principal components regression analysis.

Binary Star Reconstruction with Use of the Blind Deconvolution Method

Abstract The iterative blind deconvolution method and a modified auto-correlation method are applied to reconstruct a binary star from a speckle interferogram. The results of two methods are compared to evaluate reconstructed images. For successful reconstruction, a method of frame selection of speckle interferograms is also proposed. A suitable frame selection improves the efficiency of the reconstruction. Some results of the binary star reconstruction are demonstrated to show the usefulness of our method.

A modified autocorrelation method of linear prediction for pitch-synchronous analysis of voiced speech

A modified autocorrelation method of linear prediction is proposed for pitch-synchronous analysis of voiced speech. The method needs one full period of speech data for analysis and assumes periodic extension of the data. This method guarantees the stability of the estimated all-pole filter and is shown to perform better than the covariance and autocorrelation methods of linear prediction.

A comparative performance study of several pitch detection algorithms

A comparative performance study of seven pitch detection algorithms was conducted. A speech data base, consisting of eight utterances spoken by three males, three females, and one child was constructed. Telephone, close talking microphone, and wideband recordings were made of each of the utterances. For each of the utterances in the data base; a pitch contour was semiautomatically measured using a highly sophisticated interactive pitch detection program. The pitch contour was then compared with the pitch contour that was obtained from each of the seven programmed pitch detectors. The algorithms used in this study were 1) a center clipping, infinite-peak clipping, modified autocorrelation method (AUTOC), 2) the cepstral method (CEP), 3) the simplified inverse filtering technique (SIFT) method, 4) the parallel processing time-domain method (PPROC), 5) the data reduction method (DARD), 6) a spectral flattening linear predictive coding (LPC) method, and 7) the average magnitude difference function (AMDF) method. A set of measurements was made on the pitch contours to quantify the various types of errors which occur in each of the above methods. Included among the error measurements were the average and standard deviation of the error in pitch period during voiced regions, the number of gross errors in the pitch period, and the average number of voiced-unvoiced classification errors. For each of the error measurements, the individual pitch detectors could be rank ordered as a measure of their relative performance as a function of recording condition, and pitch range of the various speakers. Performance scores are presented for each of the seven pitch detectors based on each of the categories of error.

Comparative performance study of several pitch detection algorithms

A comparative performance study of five pitch detection algorithms was conducted. A speech data base, consisting of eight utterances spoken by three males, three females, and one child was constructed. Both telephone and wideband recordings were made of each of the utterances. For each of the utterances in the data base a “standard” pitch contour was semiautomatically measured using a highly sophistocated interactive pitch detection program. The “standard” pitch contour was then compared with the pitch contour that was obtained from each of the five programmed pitch detectors. The algorithms used in this study were (1) a center clipping, infinite-peak clipping, modified autocorrelation method; (2) the cepstral method; (3) the SIFT method; (4) the parallel processing time domain method; and (5) the data reduction method. A set of measurements were made on the pitch contours to quantify the various types of errors which occur in each of the above methods. Included among the error measurements were the average and standard deviation of the error in pitch period during voiced regions, the number of gross errors in the pitch period, and the average and standard deviation of the error in choosing onset and offset of voicing. By pooling the various error measurements, the individual pitch detectors could be rank ordered as a measure of this relative performance.