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  1. Medical and Biological Engineering and Computing
  2. Medical and Biological Engineering and Computing : Volume 38
  3. Medical and Biological Engineering and Computing : Volume 38, Issue 6, November 2000
  4. A method for turbulent noise estimation in voiced signals
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Medical and Biological Engineering and Computing : Volume 55
Medical and Biological Engineering and Computing : Volume 54
Medical and Biological Engineering and Computing : Volume 53
Medical and Biological Engineering and Computing : Volume 52
Medical and Biological Engineering and Computing : Volume 51
Medical and Biological Engineering and Computing : Volume 50
Medical and Biological Engineering and Computing : Volume 49
Medical and Biological Engineering and Computing : Volume 48
Medical and Biological Engineering and Computing : Volume 47
Medical and Biological Engineering and Computing : Volume 46
Medical and Biological Engineering and Computing : Volume 45
Medical and Biological Engineering and Computing : Volume 44
Medical and Biological Engineering and Computing : Volume 43
Medical and Biological Engineering and Computing : Volume 42
Medical and Biological Engineering and Computing : Volume 41
Medical and Biological Engineering and Computing : Volume 40
Medical and Biological Engineering and Computing : Volume 39
Medical and Biological Engineering and Computing : Volume 38
Medical and Biological Engineering and Computing : Volume 38, Issue 6, November 2000
Iterative optimal design of PET experiments for estimating β-adrenergic receptor concentration
Measurement of femoral neck anteversion in 3D. Part 1: 3D imaging method
Measurement of femoral neck anteversion in 3D. Part 2:3D modelling method
Analysis of postural sway using entropy measures of signal complexity
A method for turbulent noise estimation in voiced signals
Analysis and decomposition of signals obtained by thigh-fixed uni-axial accelerometry during normal walking
Three-dimensional mapping of brainstem functional lesions
Adaptive wavelet filtering for analysis of event-related potentials from the electro-encephalogram
Isopower mapping of electrogastrograms in short-bowel syndrome
Identification of post-myocardial infarction patients with ventricular tachycardia by time-domain intra-QRS analysis of signal-averaged electrocardiogram and magnetocardiogram
Risk evaluation of ventricular tachycardia using wavelet transform irregularity of the high-resolution electrocardiogram
Increase in pulse transit time to the foot after epidural anaesthesia treatment
Evaluation of renormalised entropy for risk stratification using heart rate variability data
Preparation of a pure autologous biodegradable fibrin matrix for tissue engineering
New model to characterise cell-substrate adhesion in the presence of osmosis
Medical and Biological Engineering and Computing : Volume 38, Issue 5, September 2000
Medical and Biological Engineering and Computing : Volume 38, Issue 4, July 2000
Medical and Biological Engineering and Computing : Volume 38, Issue 3, May 2000
Medical and Biological Engineering and Computing : Volume 38, Issue 2, March 2000
Medical and Biological Engineering and Computing : Volume 38, Issue 1, January 2000
Medical and Biological Engineering and Computing : Volume 37
Medical and Biological Engineering and Computing : Volume 36
Medical and Biological Engineering and Computing : Volume 35

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A method for turbulent noise estimation in voiced signals

Content Provider SpringerLink
Author Mitev, P. Hadjitodorov, S.
Copyright Year 2000
Abstract In this article a new acoustic parameter is introduced and it is shown that it may serve as an indicator of laryngeal function. It is termed the turbulent noise index (TNI) and is defined as 100(1− $$\bar R_{max} $$ ), where $$\bar R_{max} $$ is the mean value of the maximum correlation coefficient between each pair of consecutive glottal cycles in the voiced signal. A method for its calculation is described. Experiments with synthetic and natural voice signals show that TNI is almost independent of frequency modulation noise and amplitude modulation noise. TNI is compared with HNR (harmonic-to-noise ratio) and NNE (normalised noise energy) which require high stationarity of the voice signal and are substantially affected by slow changes of frequency and amplitude. When the parameters HNR and NNE are used to discriminate between normal and pathological voices, the overlap area contains 21.5% and 23.5% of the total number of pathological voices, respectively. Using TNI, the voices, i.e. compared to the other noise parameters TNI has a significant advantage as a diagnostic parameter.
Starting Page 625
Ending Page 631
Page Count 7
File Format PDF
ISSN 01400118
Journal Medical and Biological Engineering and Computing
Volume Number 38
Issue Number 6
e-ISSN 17410444
Language English
Publisher Springer-Verlag
Publisher Date 2000-01-01
Publisher Place Berlin, Heidelberg
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Voiced signal Turbulence noise Shimmer Jitter Autocorrelation Pathological voice Human Physiology Computer Applications Neurosciences Imaging Radiology Biomedical Engineering
Content Type Text
Resource Type Article
Subject Biomedical Engineering Computer Science Applications
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