In this paper, we propose a modified version of the autocorrelation pitch extraction method well known to be robust against noise. Utilizing that the average magnitude difference function (AMDF) has similar characteristics with the autocorrelation function, the autocorrelation function is weighted by the reciprocal of the AMDF. By simulation experiments, it is shown that the proposed pitch extr...

I N recent years, statistical process control (SPC) for autocorrelated processes has received a great deal of attention, due in part to the increasing prevalence of autocorrelation in process inspection data. With improvements in measurement and data collection technology, processes can be sampled at higher rates, which often leads to data autocorrelation. It is well known that the run length p...

Failure to allow for autocorrelation of the disturbances in a regression model can lead to biased and inconsistent parameter estimates, particularly if the model is autoregressive. While consistent estimation methods are available which allow for autocorrelation, estimation is usually much easier when there is some assurance that autocorrelation is absent. In pursuit of such assurance the prese...

In a first part is recalled a simulation method for a strictly stationary non-Gaussian process given its one-dimensional marginal distribution (or its -first statistical moments) and its autocorrelation function. This method was already widely treated in the articles [14] and [13]. The objective of the present paper is twofold : firstly simplify this method when by Mehler formula it is possible...

This simulation study examined the performance of the curve-of-factors model (COFM) when autocorrelation and growth processes were present in the first-level factor structure. In addition to the standard curve-of factors growth model, two new models were examined: one COFM that included a first-order autoregressive autocorrelation parameter, and a second model that included first-order autoregr...

The present research aimed to analyze values of the autocorrelation function measured for different time values of ground reaction forces during stable upright standing. It was hypothesized that if recording of force in time depended on the quality and way of regulating force by the central nervous system (as a regulator), then the application of autocorrelation for time series in the analysis ...

In many spatial data applications, the events at a location are highly influenced by the events at neighboring locations. In fact, this natural inclination of a variable to exhibit similar values as a function of distance between the spatial locations at which it is being measured is known as spatial dependence. Spatial autocorrelation is used to measure this spatial dependence. If the variable...

The use of higher order autocorrelations as features for pattern classification has been usually restricted to second or third orders due to high computational costs. Since the autocorrelation space is a high dimensional space we are interested in reducing the dimensionality of feature vectors for the benefit of the pattern classification task. An established technique is Principal Component An...

In this paper, we introduce a new class of robust autocorrelation estimators based on interpreting the sample autocorrelation function as a linear regression. We investigate the efficiency and robustness properties of the estimators that result from employing three common robust regression techniques. Construction of robust autocovariance and positive definite autocorrelation estimates is discu...

This paper introduces a novel way to detect metrical structure in music. We introduce a way to compute autocorrelation such that the distribution of energy in phase space is preserved in a matrix. The resulting autocorrelation phase matrix is useful for several tasks involving metrical structure. First we can use the matrix to enhance standard autocorrelation by calculating the Shannon entropy ...