scholarly journals Determination of Spectral Parameters of Speech Signal by Goertzel Algorithm

10.5772/16248 ◽  
2011 ◽  
Author(s):  
Bozo Tomas ◽  
Darko Zelenik
Keyword(s):  
Speech Signal ◽  
Spectral Parameters ◽  
Goertzel Algorithm

Development of equipments for determination of BNCT source spectral parameters

2004 ◽  
Vol 61 (5) ◽  
pp. 849-852
Author(s):  
J Burian ◽  
B Jansky ◽  
M Marek ◽  
E Novak ◽  
L Viererbl ◽  
...  
Keyword(s):  
Spectral Parameters

On the Characterization of Dynamic Properties of Random Processes by Spectral Parameters

2000 ◽  
Vol 67 (3) ◽  
pp. 519-526 ◽  
Author(s):  
G. Petrucci ◽  
M. Di Paola ◽  
B. Zuccarello
Keyword(s):  
Dynamic Properties ◽  
Time Derivative ◽  
Wide Band ◽  
Random Processes ◽  
Spectral Parameters ◽  
Unambiguous Determination ◽  
Power Spectral ◽  
Bandwidth Parameter

This paper deals with the general problem of directly relating the distribution of ranges of wide band random processes to the power spectral density (PSD) by means of closed-form expressions. Various attempts to relate the statistical distribution of ranges to the PSD by means of the irregularity factor or similar parameters have been done by several authors but, unfortunately, they have not been fully successful. In the present study, introducing the so-called analytic processes, the reasons for which these parameters are insufficient to an unambiguous determination of the range distribution and the fact that parameters regarding the time-derivative processes are needed have been explained. Furthermore, numerical simulations have shown that the range distributions depend on the irregularity factor and bandwidth parameter of both the process and its derivative. These observations are the basis for the determination of accurate relationships between range distributions and PSDs. [S0021-8936(00)02903-2]

scholarly journals Stop gap removal using spectral parameters for stuttered speech signal

2021 ◽  
Vol 10 (3) ◽  
pp. 1862-1866
Keyword(s):  
Speech Signal ◽  
Spectral Energy ◽  
Spectral Parameters ◽  
Zero Crossing ◽  
Syllable Repetition ◽  
Zero Crossing Rate ◽  
Automatic Removal

Stuttering is an involuntary disturbance in the fluent flow of speech characterized by disfluencies such as stop gaps, sound or syllable repetition or prolongation. There are high proportion of stop gaps in stuttering. This work presents automatic removal of stop gaps using combination of spectral parameters such as spectral energy, centroid, Entropy and Zero crossing rate. A method for detecting and removing stop gaps based on threshold is discussed in this paper

scholarly journals Machine learning applied to multifrequency data in astrophysics: blazar classification

2020 ◽  
Vol 498 (2) ◽  
pp. 1750-1764
Author(s):  
B Arsioli ◽  
P Dedin
Keyword(s):  
Machine Learning ◽  
Permutation Test ◽  
Spectral Characteristics ◽  
Peak Frequency ◽  
Support Vector ◽  
Spectral Parameters ◽  
Nearest Neighbours ◽  
Svm Algorithm

ABSTRACT The study of machine learning (ML) techniques for the autonomous classification of astrophysical sources is of great interest, and we explore its applications in the context of a multifrequency data-frame. We test the use of supervised ML to classify blazars according to its synchrotron peak frequency, either lower or higher than 1015 Hz. We select a sample with 4178 blazars labelled as 1279 high synchrotron peak (HSP: $\rm \nu$-peak > 1015 Hz) and 2899 low synchrotron peak (LSP: $\rm \nu$-peak < 1015 Hz). A set of multifrequency features were defined to represent each source that includes spectral slopes ($\alpha _{\nu _1, \nu _2}$) between the radio, infra-red, optical, and X-ray bands, also considering IR colours. We describe the optimization of five ML classification algorithms that classify blazars into LSP or HSP: Random forests (RFs), support vector machine (SVM), K-nearest neighbours (KNN), Gaussian Naive Bayes (GNB), and the Ludwig auto-ML framework. In our particular case, the SVM algorithm had the best performance, reaching 93 per cent of balanced accuracy. A joint-feature permutation test revealed that the spectral slopes alpha-radio-infrared (IR) and alpha-radio-optical are the most relevant for the ML modelling, followed by the IR colours. This work shows that ML algorithms can distinguish multifrequency spectral characteristics and handle the classification of blazars into LSPs and HSPs. It is a hint for the potential use of ML for the autonomous determination of broadband spectral parameters (as the synchrotron ν-peak), or even to search for new blazars in all-sky data bases.

Quantitative Analysis of Mixed Volatile Fluids by Raman Microprobe Spectroscopy: A Cautionary Note on Spectral Resolution and Peak Shape

1993 ◽  
Vol 47 (6) ◽  
pp. 816-820 ◽  
Author(s):  
Jeffery C. Seitz ◽  
Jill D. Pasteris ◽  
George B. Morgan
Keyword(s):  
Spectral Resolution ◽  
Quantitative Information ◽  
Peak Height ◽  
Peak Position ◽  
Accurate Analysis ◽  
Spectral Parameters ◽  
Peak Areas ◽  
Selection Of ◽  
Modest Effect

Raman analyses of fluid inclusions can yield quantitative information on composition (from peak areas and heights) and density (from peak position and width). In this study, we examine the effect of instrumental spectral resolution on the ratios of these spectral parameters, and the selection of appropriate integration limits for the determination of peak areas in the CO2-CH4-N2 system. Spectral resolution was varied from about 1 to 9 cm−1 by co-varying the widths of all spectrometer slits. Changes in resolution produced a modest effect on peak-area ratios and a significant effect on peak-height ratios. Measured peak-width ratios varied strongly as a function of the spectral resolution. In addition, we observed a moderate shift in the measured peak position of N2, which can be related to the asymmetry of the band. These results indicate that accurate analysis requires careful attention to the selection of quantification factors, especially if the selected values were derived from studies at different spectral resolutions. Another factor that can have a significant effect on the calculated compositions of CH4- and H2-bearing fluid mixtures is the band broadening that occurs with increasing pressure.

scholarly journals Cynomolgus and rhesus monkey visual pigments. Application of Fourier transform smoothing and statistical techniques to the determination of spectral parameters.

1987 ◽  
Vol 89 (5) ◽  
pp. 717-743 ◽  
Author(s):  
F I Hárosi
Keyword(s):  
Fourier Transform ◽  
Macaca Fascicularis ◽  
Statistical Tests ◽  
Linear Dichroism ◽  
Cell Types ◽  
Spectral Parameters ◽  
Population Means ◽  
Visual Cells ◽  
Cone Pigments

Microspectrophotometric measurements were performed on 217 photoreceptors from cynomolgus, Macaca fascicularis, and rhesus, M. mulatta, monkeys. The distributions of cell types, for rods and blue, green, and red cones were: 52, 12, 47, and 44, respectively, for the cynomolgus, and 22, 4, 13, and 13 for the rhesus. Visual cells were obtained fresh (unfixed), mounted in various media (some containing 11-cis-retinal), and then located visually under dim red (650 nm) illumination. Absolute absorbance (A), linear dichroism (LD), and bleaching difference (BD) absorbance spectra were recorded through the sides of outer segments. The spectra were subjected to rigorous selection criteria, followed by digital averaging and Fourier transform filtering. Statistical methods were also applied to the accepted samples in the estimation of population means and variances. The wavelength of mean peak absorbance (lambda max) and the standard error at 95% certainty of the rod and blue, green, and red cone pigments in cynomolgus were 499.7 +/- 2.5, 431.4 +/- 4.2, 533.9 +/- 2.4, and 565.9 +/- 2.8 nm, respectively. The rhesus pigments were statistically indistinguishable from the cynomolgus, having lambda max of approximately 500, 431, 534, and 566 nm. Statistical tests did not reveal the presence of a lambda max subpopulation (i.e., anomalous pigments). The bandwidth of each alpha-band was determined in two segments, giving rise to the longwave half-density (LWHDBW), shortwave half-density (SWHDBW), and total half-density (THDBW) bandwidths. The LWHDBW was found to have the smallest variance. Both the LWHDBW and the THDBW showed linear dependence on the peak wavenumber (lambda max)-1 for the four macaque pigments.

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