Statistical Analysis of Linear Filters Driven By Non-White Noise

2001 ◽  
Vol 56 (6-7) ◽  
pp. 14
Author(s):  
Vladimir A. Kazakov ◽  
Ruben P. Hernandez
Keyword(s):  
Statistical Analysis ◽  
White Noise ◽  
Linear Filters

Effects of Four Noise Competitors on the California Consonant Test

1979 ◽  
Vol 44 (3) ◽  
pp. 354-362 ◽  
Author(s):  
Jeffrey L. Danhauer ◽  
Jonathan G. Leppler
Keyword(s):  
Statistical Analysis ◽  
White Noise ◽  
Normal Hearing ◽  
State University ◽  
Speech Discrimination ◽  
Cocktail Party ◽  
Signal To Noise ◽  
Bowling Green State University ◽  
Listening Levels ◽  
Audiologic Testing

Thirty-five normal-hearing listeners' speech discrimination scores were obtained for the California Consonant Test (CCT) in four noise competitors: (1) a four-talker complex (FT), (2) a nine-talker complex developed at Bowling Green State University (BGMTN), (3) cocktail party noise (CPN), and (4) white noise (WN). Five listeners received the CCT stimuli mixed ipsilaterally with each of the competing noises at one of seven different signal-to-noise ratios (S/Ns). Articulation functions were plotted for each noise competitor. Statistical analysis revealed that the noise types produced few differences on the CCT scores over most of the S/Ns tested, but that noise competitors similar to peripheral maskers (CPN and WN) had less effect on the scores at more severe levels than competitors more similar to perceptual maskers (FT and BGMTN). Results suggest that the CCT should be sufficiently difficult even without the presence of a noise competitor for normal-hearing listeners in many audiologic testing situations. Levels that should approximate CCT maximum discrimination (D-Max) scores for normal listeners are suggested for use when clinic time does not permit the establishment of articulation functions. The clinician should determine the S/N of the CCT tape itself before establishing listening levels.

Threshold of Octave Masking as a Predictor of Temporary Threshold Shift Following Repeated Noise Exposure

1984 ◽  
Vol 49 (3) ◽  
pp. 303-308 ◽  
Author(s):  
Gail D. Chermak ◽  
Joan E. Dengerink ◽  
Harold A. Dengerink
Keyword(s):  
Statistical Analysis ◽  
White Noise ◽  
Noise Exposure ◽  
Repeated Exposure ◽  
Cumulative Effects ◽  
Threshold Shift ◽  
Temporary Threshold Shift ◽  
Increased Susceptibility ◽  
Female Subjects

Cumulative auditory effects of repeated exposure to a 3-min 110 dB SPL white noise were examined in 20 subjects (10 male, 10 female). Statistical analysis revealed greater 8-kHz temporary threshold shift in female ears despite equivalent preexposure 8-kHz thresholds across gender. Cumulative effects were seen in thresholds of octave masking scores, especially in female subjects, possibly reflecting increased susceptibility following the second noise exposure.

scholarly journals Modeling the Clustering Volatility of India’s Wholesale Price Index and the Factors Affecting It

2016 ◽  
Vol 6 (1) ◽  
pp. 141
Author(s):  
Mohammad Naim Azimi
Keyword(s):  
Statistical Analysis ◽  
White Noise ◽  
Price Index ◽  
Wholesale Price ◽  
P Value ◽  
External Shocks ◽  
Factors Affecting ◽  
Arch Effect ◽  
A Value ◽  
Wholesale Price Index

<p>This paper proposes to examine the clustering volatility of India’s Wholesale Price Index throughout the period 1960 to 2014 by applying the ARCH (1) and GARCH (1) model. The pre-conditional requirement for the computation of ARCH (1, 1) required us to perform several other tests i.e. Dickey Fuller, Ordinary Least Squared Regression and post OLS tests for investigating the ARCH effect in the first difference of WPI. The statistical analysis reveals a <em>p-value</em> of 0.569 for the GARCH mean model which is not significant at ∂ 0.05 to explain that the previous period’s volatility can influence the WPI. The coefficient of WPI at first difference exhibits a value of less than 1 which is nice in magnitude with a <em>p</em>-value of 0.005 for ARCH at ∂ 0.05 which is significant to explain the volatility of the WPI. The diagnostic test of autocorrelation in the residuals reveals that the residuals are white noise by exhibiting a corresponding probability value of 0.3757. Since, the overarching objective of this paper is to examine the clustering volatility of the aforementioned variable with regards to the internal shocks, there might have been other factors of external shocks on WPI that have deliberately been overlooked in this paper.</p>

Response of Rotors Subjected to Random Support Excitations

1985 ◽  
Vol 107 (4) ◽  
pp. 453-459 ◽  
Author(s):  
R. Subbiah ◽  
R. B. Bhat ◽  
T. S. Sankar
Keyword(s):  
Statistical Analysis ◽  
White Noise ◽  
Modal Analysis ◽  
Rotating Machines ◽  
Rotor Systems ◽  
Earthquake Motion ◽  
Noise Type ◽  
Power Spectral ◽  
Power Spectral Densities ◽  
Cross Correlations

Response of rotor systems subjected to random support excitations is studied. Support excitations may occur for rotating machines installed either on the ground due to earthquake motion, or on board moving systems such as ships. These excitations in general are random in nature and hence the response of the machine can be obtained following a statistical analysis. In this paper, the amplitude power spectral densities (PSD) due to random support excitations are obtained for rotor systems, using modal analysis methods. The excitations are assumed to be stationary and Gaussian with a white noise type of PSD. Excitations through different supports are assumed to be statistically independent so that their cross correlations are zero. Results for rotors with various parametric combinations are presented.

scholarly journals A Noise Generator for Embedded Circuits Testing

2004 ◽  
Vol 1 (1) ◽  
pp. 38-43
Author(s):  
Maria da Glória Flores ◽  
Marcelo Negreiros ◽  
Luigi Carro ◽  
Altamiro Susin
Keyword(s):  
White Noise ◽  
Input Signal ◽  
Spectral Response ◽  
Noise Generator ◽  
Test Method ◽  
Electronic Systems ◽  
Linear Filters ◽  
Analog To Digital ◽  
White Noise Generator ◽  
Reference Input

This paper describes the implementation of a white noise generator to be used as the input signal of a new method for testing analog-to-digital converters (ADCs) and linear filters. The main goal of this method is to avoid the comparison of the output signal with a known and very precise reference input. The proposed white noise generator is easily implemented, with less complexity than others excitation signals. The use of noise as the input signal avoids concerns about the inherent noise present in all electronic systems. The testing technique is based on the analysis of the spectral response of the CUT output. This paper covers the generation of the excitation signal, as well as simulation and practical results are presented to prove the efficiency of the test method.

Übertragungseigenschaften im Auswertesystem für das Bewegungssehen II

1967 ◽  
Vol 22 (12) ◽  
pp. 1343-1351 ◽  
Author(s):  
Dezsö Varju ◽  
Werner Reichardt
Keyword(s):  
White Noise ◽  
Response Curve ◽  
Linear Filters ◽  
Noise Pattern ◽  
Stimulus Response ◽  
Transfer Characteristics ◽  
Algebraic Error ◽  
Low Pass ◽  
Contrast Pattern ◽  
Low Pass Filters

Some years ago a quantitative analysis of the optomotor responses in the beetle Chlorophanus was worked out. This analysis has led to the formulation of a model for the functional structure of perception describing stimulus response relations. The model consists of linear filters with different transfer characteristics and correlator units. The transfer characteristics of the filters was determined on the basis of experiments with rotating patterns of sinusoidally changing contrasts. The model enabled us to predict responses to moving patterns of various contrast configurations. In one of the predictions the response curve to the movements of a “white noise“ contrast pattern was calculated and compared with reactions of the beetle which were elicited by a rotating cylinder carrying randomly distributed (vertically oriented) striped shades from white through black 1. The comparison showed rough agreement between theory and experiment. Recently THORSON has pointed out that Equ. (38) in our 1959 paper 1, which gives the analytic expression for the response to a moving “white noise“ pattern, contains an algebraic error. Prompted by this finding, we have again taken up this part of the investigation in the present paper. If the algebraic error is corrected and the calculated response curve plotted, a misfit between the theoretically derived response and the experimental data is found. If however, the actual Fourier spectrum of the pattern is used in the calculations instead of the hypothetical “white noise“, an acceptable fit is achieved. The approximation can be improved if the time constants τF and τH of the low pass filters of the model are slightly changed from the formerly obtained values. Furthermore it was investigated whether a reduced model, containing only one or two different filters lead to better approximations. The reported results favour the model originally suggested containing three different types of filters.

Integrated Fractional white Noise as an Alternative to Multifractional Brownian Motion

2007 ◽  
Vol 44 (02) ◽  
pp. 393-408 ◽  
Author(s):  
Allan Sly
Keyword(s):  
Stochastic Process ◽  
Brownian Motion ◽  
White Noise ◽  
Gaussian Process ◽  
Discrete Data ◽  
Hölder Exponent ◽  
Scaling Properties ◽  
Multifractional Brownian Motion ◽  
Local Properties

Multifractional Brownian motion is a Gaussian process which has changing scaling properties generated by varying the local Hölder exponent. We show that multifractional Brownian motion is very sensitive to changes in the selected Hölder exponent and has extreme changes in magnitude. We suggest an alternative stochastic process, called integrated fractional white noise, which retains the important local properties but avoids the undesirable oscillations in magnitude. We also show how the Hölder exponent can be estimated locally from discrete data in this model.

Statistical analysis of classification

1966 ◽  
Vol 24 ◽  
pp. 188-189
Author(s):  
T. J. Deeming
Keyword(s):  
Multivariate Analysis ◽  
Statistical Analysis ◽  
Classification Scheme ◽  
Analytical Procedure ◽  
Narrow Band ◽  
Scientific Research ◽  
Maximum Amount ◽  
Amount Of Information

If we make a set of measurements, such as narrow-band or multicolour photo-electric measurements, which are designed to improve a scheme of classification, and in particular if they are designed to extend the number of dimensions of classification, i.e. the number of classification parameters, then some important problems of analytical procedure arise. First, it is important not to reproduce the errors of the classification scheme which we are trying to improve. Second, when trying to extend the number of dimensions of classification we have little or nothing with which to test the validity of the new parameters.Problems similar to these have occurred in other areas of scientific research (notably psychology and education) and the branch of Statistics called Multivariate Analysis has been developed to deal with them. The techniques of this subject are largely unknown to astronomers, but, if carefully applied, they should at the very least ensure that the astronomer gets the maximum amount of information out of his data and does not waste his time looking for information which is not there. More optimistically, these techniques are potentially capable of indicating the number of classification parameters necessary and giving specific formulas for computing them, as well as pinpointing those particular measurements which are most crucial for determining the classification parameters.

Quantitative parallel EELS spectrum imaging developed as a new tool in AEM

1992 ◽  
Vol 50 (2) ◽  
pp. 1202-1203
Author(s):  
Gianluigi Botton ◽  
Gilles L'espérance
Keyword(s):  
Statistical Analysis ◽  
Spatial Resolution ◽  
Personal Computer ◽  
Systematic Study ◽  
Present Author ◽  
Chemical Elements ◽  
Detection Limits ◽  
Research Groups ◽  
Quantitative Performance ◽  
Spectrum Imaging

As interest for parallel EELS spectrum imaging grows in laboratories equipped with commercial spectrometers, different approaches were used in recent years by a few research groups in the development of the technique of spectrum imaging as reported in the literature. Either by controlling, with a personal computer both the microsope and the spectrometer or using more powerful workstations interfaced to conventional multichannel analysers with commercially available programs to control the microscope and the spectrometer, spectrum images can now be obtained. Work on the limits of the technique, in terms of the quantitative performance was reported, however, by the present author where a systematic study of artifacts detection limits, statistical errors as a function of desired spatial resolution and range of chemical elements to be studied in a map was carried out The aim of the present paper is to show an application of quantitative parallel EELS spectrum imaging where statistical analysis is performed at each pixel and interpretation is carried out using criteria established from the statistical analysis and variations in composition are analyzed with the help of information retreived from t/γ maps so that artifacts are avoided.

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