Unambiguous decomposition of a complex spectral contour into two components

1978 ◽  
Vol 29 (5) ◽  
pp. 1402-1404
Author(s):  
B. N. Grechushnikov ◽  
I. N. Kalinkina
Keyword(s):  
Spectral Contour

A new method for breaking down a complex spectral contour into two symmetry bands

1973 ◽  
Vol 19 (2) ◽  
pp. 1094-1096
Author(s):  
V. I. Mikhailenko ◽  
E. I. Kucherenko ◽  
M. V. Kotov
Keyword(s):  
New Method ◽  
Spectral Contour

Deconvolution Technique Application to Spectral Contour Analysis

1983 ◽  
Vol 16 (10) ◽  
pp. 753-763 ◽  
Author(s):  
G. Georgiev ◽  
T. Kalkanjiev ◽  
V. Petrov
Keyword(s):  
Contour Analysis ◽  
Spectral Contour ◽  
Deconvolution Technique

Multivariate Statistical Analysis of Flat Vowel Spectra With a View to Characterizing Dysphonic Voices

2000 ◽  
Vol 43 (6) ◽  
pp. 1493-1508 ◽  
Author(s):  
Jean Schoentgen ◽  
Mounir Bensaid ◽  
Fabrizio Bucella
Keyword(s):  
Statistical Analysis ◽  
Principal Components ◽  
Multivariate Statistical Analysis ◽  
Heuristic Search ◽  
Vocal Tract ◽  
Multivariate Statistical ◽  
Magnitude Spectrum ◽  
Spectral Contour ◽  
Components Analysis ◽  
Different Levels

The aim of this article is to show how dysphonic voices can be characterized by means of a multivariate statistical analysis of flat vowel spectra. The spectral contour was obtained by means of a wavelet transform of the logarithmic magnitude spectrum, which was subsequently flattened to remove interspeaker variability related to the excitation and vocal tract filter functions. The results of the statistical analysis of flat spectra were the following. Firstly, principal components analysis produced markers that separated noisy from clean spectra. Secondly, the heuristic search for harmonic peaks or interharmonic dips could be omitted. Thirdly, conventional spectral markers of noise appeared as special instances of the markers that were derived statistically. Fourthly, the levels of visually assigned hoarseness and the first two principal components were significantly correlated. The assignment of different levels of (visual) hoarseness to different vowel timbres could be explained by the variability associated with the spectral contour.

Spectral Contour Broadening upon the Generation of the Second Harmonic

2020 ◽  
Vol 84 (7) ◽  
pp. 751-754
Author(s):  
S. V. Alekseev ◽  
N. G. Ivanov ◽  
V. F. Losev
Keyword(s):  
Second Harmonic ◽  
Spectral Contour

Simple algorithm for resolving a complex spectral contour into elementary symmetric bands

1986 ◽  
Vol 44 (4) ◽  
pp. 410-414
Author(s):  
V. I. Mikhailenko ◽  
V. V. Mikhal'chuk
Keyword(s):  
Simple Algorithm ◽  
Spectral Contour

Linear Scaling Self-Consistent Field Theory with Spectral Contour Accuracy

2019 ◽  
Vol 8 (11) ◽  
pp. 1402-1406 ◽  
Author(s):  
Daniel L. Vigil ◽  
Carlos J. García-Cervera ◽  
Kris T. Delaney ◽  
Glenn H. Fredrickson
Keyword(s):  
Field Theory ◽  
Linear Scaling ◽  
Spectral Contour ◽  
Consistent Field ◽  
Self Consistent ◽  
Self Consistent Field ◽  
Contour Accuracy ◽  
Self Consistent Field Theory
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