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Pitch Extraction
Published in Randy Goldberg, Lance Riek, A Practical Handbook of Speech Coders, 2019
The autocorrelation function is frequently used for pitch extraction. A correlation function is a measure of the degree of similarity between two signals. The autocorrelation measures how well the input signal matches with a time-shifted version of itself. The maxima of the autocorrelation function occur at intervals of the pitch period of the original signal.
An evaluation method of methodology for integration of HALT, HASS and ADT
Published in Stein Haugen, Anne Barros, Coen van Gulijk, Trond Kongsvik, Jan Erik Vinnem, Safety and Reliability – Safe Societies in a Changing World, 2018
Tianji Zou, Peng Li, Wei Dang, Kai Liu, Ge Zhang
Correlation function is a function that gives the statistical correlation between random variables. Sometimes correlation functions of different random variables are referred to as cross-correlation functions to emphasize that different variables are being considered. Through analyzing the correlation function, the correlativity between posterior distributions and prior distributions can be weighted.
Estimation of Stochastic Process Variance
Published in Vyacheslav Tuzlukov, Signal Processing in Radar Systems, 2017
Analogous statement appears for a set of problems in statistical theory concerning optimal signal processing in high noise conditions. In particular, given the accurate measurement of the stochastic process variance, it is possible to detect weak signals in powerful noise within the limits of a short observation interval [0, T]. In line with this fact, in [1] it was assumed that for the purpose of resolving detection problems, including problems related to zero errors, we should reject the accurate knowledge of the correlation function of the observed stochastic processes or we need to reject the accurate measurement of realizations of the input stochastic process. Evidently, in practice these two factors work. However, depending on which errors are predominant in the analysis of errors, limitations arise due to insufficient knowledge about the correlation function or due to inaccurate measurement of realizations of the investigated stochastic process.
An external focus of attention promotes flow experience during simulated driving
Published in European Journal of Sport Science, 2019
David J. Harris, Samuel J. Vine, Mark R. Wilson
Data processing was conducted in Matlab (vR2016a). To compute time lag and cross-correlation in eye-steering coordination, x-axis gaze coordinates and wheel movements (in degrees) were time locked and filtered using a lowpass moving average filter. The cross-correlation function measures the degree of similarity across shifted sequences of the corresponding vector, as a function of the time lag. The peak lagged correlation indicates the average time lag between eyes and wheel, and r the degree of correlation between the signals. Sample entropy of the de-noised wheel signal was then calculated, using a tolerance of 0.2*standard deviation of the sample (Richman & Moorman, 2000).
Surrogate model–based optimal feed-forward control for dimensional-variation reduction in composite parts' assembly processes
Published in Journal of Quality Technology, 2018
The correlation function represents the basic principle that when two inputs are close to each other in Euclidean distance, the correlation between the corresponding outputs will be high. This principle is commonly observed in the dimensional errors of composite parts. As a result, the uncertainty related to the predictions is small for input values that are similar to the training data set and large for input values that are far from the training data set.
A signal analysis based hunting instability detection methodology for high-speed railway vehicles
Published in Vehicle System Dynamics, 2021
Jianfeng Sun, Enrico Meli, Wubin Cai, Hongxin Gao, Maoru Chi, Andrea Rindi, Shulin Liang
The correlation analysis methods for vibration signals mainly rely on the concepts of correlation function and correlation coefficient to study the correlation and the mutual dependence between two signals. The cross-correlation coefficient has been widely used in the signal processing field to explore the similarity of two signals or determine the positions of vibration source via the time lag [27–29].