Data compression by linear prediction for storage and transmission of EEG signals

The article presents a proposal to use linear prediction method for a quick analysis of surface myoelectric (EMG) signals. The spectra obtained with the linear prediction (LP) and Fourier methods were compared. The LP method allows for a precise determination of the location and amplitude of the spectrum maximum and observation of changes in muscle tension and contraction phases. EMG spectra of brachial biceps during flexion and extension of the forearm by four adults were analyzed. The optimal width of the time window for the averaging of motor unit action potentials that allows for the observation of changes during contraction was established. It has been found that maximum spectrum during flexion has a significantly higher frequency and amplitude than during the extension of the forearm.

Maximum posterior probability (MAP) demodulation with delay of an FM signal in the presence of noise is realized and compared with a) Griffiths' linear prediction method, b) a complex-valued version of Griffiths' method, c) three new modifications of Griffiths' method which can improve the performance of this method, and d) maximum-likelihood (ML) frequency estimation. These first quantitative results on MAP demodulation with delay show an advantage of a few db in output SNR at high input CNR and much larger advantages at low input CNR, relative to the other techniques.

In spite of its extensive use, speech analysis based on linear prediction (LP) is liable to various causes of inaccuracy. This paper presents a novel approach to improve the accuracy in the estimation of the voiced speech production model based on the LP method. The presented method uses interpolation between spectral points which are least influenced by artifacts in the spectral analysis and by noise in the signal. We show, on analyses of both synthetic and natural speech, that the averaged parabolic approximation between harmonic peaks of voiced speech spectrum reduces the sensitivity of the LP analysis to changes in the fundamental frequency Fo and to noise. The method is well suited for combination with the Spectral Transform LP method, previously proposed by the authors [1].

Khasi Speech Recognition using Hidden Markov Model with Different Spectral Features: A Comparison

Students of economic time series recognize the notion of a -a point in time when a series which had been increasing reverses and, for a time, decreases.' An example of a turning point is given in figure 1 where the time series of quarterly, seasonally adjusted real GNP is shown. The time points 1957:3 and 1973:4 can be considered turning points since they each mark the beginning of a downturn. For economists, turning points have long been an object of study and comment (Zarnowitz 1972). Turning points have received relatively less attention from statisticians. Thus, the term is not found in the indexes of Wiener (1949), Yaglom (1962), Whittle (1963), Box and Jenkins (1970), Hannan (1970), Anderson (1971), Brillinger (1975), or Fuller (1976). More to the point (though more subjectively), the concept of a turning point is absent as well. In Section II of this paper I discuss the relationship between the theory of minimum mean square error linear prediction and the turning point prediction problem. I show why the linear prediction theory does not give a solution to the Linear least-squares prediction methods are not directly applicable to the prediction of time series points. In this paper the linear least-squares technique is extended to allow computation of the distribution of the turning points of a time series, conditional on past observations. The method is illustrated using quarterly seasonally adjusted GNP.

The support vector machine, NARX neural network and combination prediction method are used to predict the railway freight volume in this paper. The impact factors of the railway freight volume are analyzed. Two single prediction models: support vector machine model and NARX neural network model are built to predict railway freight volume. Based on it, the linear combination prediction method is adopted to predict the railway freight volume and get better predicted results compared to the single prediction method. The linear combination prediction method is able to adapt to the railway freight volume prediction problem better, and could provide some references for the railway planning and the decision-making departments.

Genome-enabled prediction of meat and carcass traits using Bayesian regression, single-step genomic best linear unbiased prediction and blending methods in Nelore cattle

Contribution to the Definition of the Power Bands Limits of Sleep EEG by Linear Prediction

Linear prediction methods, such as least squares for regression, logistic regression and support vector machines for classification, have been extensively used in statistics and machine learning. In this paper, we study stochastic gradient descent (SGD) algorithms on regularized forms of linear prediction methods. This class of methods, related to online algorithms such as perceptron, are both efficient and very simple to implement. We obtain numerical rate of convergence for such algorithms, and discuss its implications. Experiments on text data will be provided to demonstrate numerical and statistical consequences of our theoretical findings.

Adaptive signal processing methods are presented in support of a noninvasive ambulatory fetal heart rate monitor. Adaptive least mean square (LMS) linear prediction methods are used for fetal heart tone signature analysis and detection in the presence of background acoustic noise. The signal processing techniques designed to identify, analyze, and detect the fetal phonocardiographic signature are discussed. Subsequent evaluation of the detected fetal heart tone events are used to determine the instantaneous heart rate. Preliminary investigation has indicated that linear prediction is feasible for detecting the fetal heart tones in an advanced acoustic fetal heart rate monitor. A prediction length of eight was found to be suboptimal in minimizing the total mean square error over the training event. >

Investigates estimation problem of sin wave frequencies. The forward-backward linear prediction method (FBLP1) with least-absolute error criterion is used to improve the frequency estimate performance attained by linear prediction method (FBLP2) with least-square error criterion. Theoretical and computer simulations show that FBLP1 method has performances similar and superior to FBLP2 method.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

This paper introduces a modified linear prediction method based on the Karhumen-Loeve expansion of the correlation matrix of the speech samples. This result is obtained via a new normalization of the parameters. It is shown that, due to some important properties of Toeplitz matrices, the poles of the A. R. model lie on the unit circle. Consequently, only the formant frequencies are computed and the result can be interpreted as a special discrete Fourier transform. Application to speech analysis is developped with a comparison to the usual linear prediction and ceptrum methods.

In this study, formant tracking is investigated by refining the formants tracked by an existing data-driven tracker, DeepFormants, using the formants estimated in a model-driven manner by linear prediction (LP)-based methods. As LP-based formant estimation methods, conventional covariance analysis (LP-COV) and the recently proposed quasi-closed phase forward–backward (QCP-FB) analysis are used. In the proposed refinement approach, the contours of the three lowest formants are first predicted by the data-driven DeepFormants tracker, and the predicted formants are replaced frame-wise with local spectral peaks shown by the model-driven LP-based methods. The refinement procedure can be plugged into the DeepFormants tracker with no need for any new data learning. Two refined DeepFormants trackers were compared with the original DeepFormants and with five known traditional trackers using the popular vocal tract resonance (VTR) corpus. The results indicated that the data-driven DeepFormants trackers outperformed the conventional trackers and that the best performance was obtained by refining the formants predicted by DeepFormants using QCP-FB analysis. In addition, by tracking formants using VTR speech that was corrupted by additive noise, the study showed that the refined DeepFormants trackers were more resilient to noise than the reference trackers. In general, these results suggest that LP-based model-driven approaches, which have traditionally been used in formant estimation, can be combined with a modern data-driven tracker easily with no further training to improve the tracker’s performance.

Linear prediction has been established as a reliable and accurate technique for spectrum estimation. One of its most interesting properties is its high resolution. A computer simulation of a signal processor based on this method has been implemented in order to study the errors associated with the estimation of the spectral center frequency of narrow-band processes. Both the Yule-Walker, or correlation method, and Burg's method were considered. The sensitivity of the results was determined for variations in the signal-to-noise ratio, number of samples, process bandwidth and relative center frequency. The results are then compared to lower bounds on spectral center frequency estimation based on periodogram analysis and radar principles.

High-resolution methods are generally defined to be high-performance methods for estimating and/or detecting the desired and/or undesired signal components present in a given set of data. The term “high-resolution” also implies a good ability to resolve very “similar” signal components. One of the most common problems in signal processing is known as frequency estimation. In frequency estimation, “high-resolution” often refers to a good ability to resolve two or more closely located frequencies in the given data. There are two groups of high-resolution methods. One is parametric methods, and the other non-parametric methods. The parametric high-resolution methods result from ingenious exploitations of known data structures. The non-parametric high-resolution methods maximize the output of some desired information with little knowledge of the data structure. The choice between parametric methods and non-parametric methods largely depends on one’s confidence in the assumed data model. In this chapter, we expose the readers to a range of existing parametric high-resolution methods. In Section 1.2, we present several frequency estimation techniques using algebraic principles. They are linear prediction, matrix pencil, and iterative quadratic maximum likelihood. The linear prediction method and the matrix pencil method can achieve near-optimal accuracy of estimation without the local convergence issues associated with the optimal methods. The computational complexities of the two methods are among the most efficient. The iterative quadratic maximum likelihood method is an approximation of the (exact) maximum likelihood method. Under some condition (e.g., high SNR), this approximation achieves the optimal accuracy. Concepts like forwardand-backward averaging, total least square, and (joint) singular value decomposition of orthonormal matrices will also be discussed. In Section 1.3, we present methods that exploit large sample theorems in statistics. In particular, we focus on data of multiple independent measurements. The key data structure is captured by the dominant (principal) subspace of the data matrix or the dominant eigenvectors of the data covariance matrix. The principal subspace is referred