Fundamental frequency estimation of voice of patients with laryngeal disorders

Mitev, Petar; Hadjitodorov, Stefan

doi:10.1016/s0020-0255(03)00161-0

Cited by 23 publications

(14 citation statements)

References 18 publications

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“…Name Citation fo/GCI URL N/A a nonlinear algorithm for epoch marking in speech signals using poincare maps Mann and McLaughlin (1998) fo N/A event-based instantaneous fundamental frequency estimation B. Yegnanarayana and K. Murty (2009) fo N/A harmonics frequency estimation based on instantaneous frequency Abe, Kobayashi, and Imai (1995) fo N/A hilbert envelope of linear prediction residual Guruprasad, B. Yegnanarayana, and K. Sri Rama Murty (2007) GCI N/A hidden Markov-model multipitch tracking algorithm Mingyang Wu, DeLiang Wang, and Brown (2003) fo N/A improved time domain pitch detection algorithm for pathological voice Jamludin et al (2012) fo N/A maximum likelihood harmonic matching and hidden Markov models Doval and Rodet (1993) fo N/A method to determine the instants of significant excitation using the average group delay characteristics of minimum phase signals Smits and B. Yegnanarayana (1995) GCI N/A multiband statistical learning Sha, Burgoyne, and Saul (2004) fo N/A optimum comb method Moorer (1974) fo N/A period histogram Schroeder (1968) fo N/A Poincaré sections for pitch mark determination Hagmüller and Kubin (2005) fo N/A real time harmonic pitch detector Seneff (1978) fo ID Name Citation fo/GCI URL N/A robust pitch determination using nonlinear state-space embedding Terez (2002) fo N/A spectral autocorrelation method Lahat, Niederjohn, and Krubsack (1987) fo N/A spectral equalization LPC method using Newton's transformation Atal, unpublished, cited in L. R. Rabiner and Crochiere (1976) fo N/A statistical pitch detection algorithm Y.-R. Wang, Wong, and Tsao (2002) fo N/A synthesis-based method for pitch extraction Paliwal and P. Rao (1983) fo N/A tunable IIR filter Lane (1990) fo N/A two-level autocorrelation method Mitev and Hadjitodorov (2003) fo (e)SRPD Super-Resolution Pitch Determinator Medan, Yair, and Chazan (1991) fo URL ACF-AMDF pitch detection Scheme based on ACF and AMDF Kumar, Bhattacharya, and Patel (2014) fo AGCD Approximate Greatest Common Devisor algorithm Sreenivas and P. V. S. Rao (1979) fo AMDF Average Magnitude Difference Function Ross et al (1974) fo ASDF Average Squared Difference Function…”

Section: Idmentioning

confidence: 99%

Fundamental Frequency Estimation of Low-quality Electroglottographic Signals

Herbst

Dunn

2019

Journal of Voice

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Fundamental frequency (f) is often estimated based on electroglottographic (EGG) signals. Because of the nature of the method, the quality of EGG signals may be impaired by certain features like amplitude or baseline drifts, mains hum, or noise. The potential adverse effects of these factors on f estimation have to date not been investigated. Here, the performance of 13 algorithms for estimating f was tested, based on 147 synthesized EGG signals with varying degrees of signal quality deterioration. Algorithm performance was assessed through the standard deviation σ of the difference between known and estimated f data, expressed in octaves. With very few exceptions, simulated mains hum, and amplitude and baseline drifts did not influence f results, even though some algorithms consistently outperformed others. When increasing either cycle-to-cycle f variation or the degree of subharmonics, the SIGMA algorithm had the best performance (max. σ = 0.04). That algorithm was, however, more easily disturbed by typical EGG equipment noise, whereas the NDF and Praat's auto-correlation algorithms performed best in this category (σ = 0.01). These results suggest that the algorithm for f estimation of EGG signals needs to be selected specifically for each particular data set. Overall, estimated f data should be interpreted with care.

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Section: Idmentioning

confidence: 99%

Fundamental Frequency Estimation of Low-quality Electroglottographic Signals

Herbst

Dunn

2019

Journal of Voice

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“…Qualquer tipo de lesão ou alteração nas pregas vocais provocará alterações na qualidade da voz. É por esse motivo que algumas pesquisas ressaltam a importância de se utilizar a análise acústica vocal como uma técnica não invasiva e capaz de fornecer suporte ao diagnóstico das disfunções laríngeas [9], [10], [11], [12].…”

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Assessment of laryngeal disorders through the global energy of speech

Fernandes

Mattioli

Lamounier

et al. 2011

IEEE Latin Am. Trans.

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Voice analysis is an important tool in the diagnosis of laryngeal disorders. Among distinct signal processing techniques employed for voice analysis, the spectrogram is commonly used, as it allows for a visualization of the variation of the energy of the signal as a function of both time and frequency. In this context, this study investigates the use of the global energy of the signal, estimated through the spectrogram, as a tool for discrimination between signals obtained from healthy and pathological subjects. This research has also exploited the potential use of the global energy of the voice signal to discriminate distinct laryngeal disorders. In total, 94 subjects were involved in this study, from which 46 were dysphonic and 48 normal. The diagnosis of laryngeal disorders was confirmed by means of a videolaryngoscopic examination. Participants were also subjected to a clinical examination of vocal acoustic through the recording of the sustained vowel /ε/. The global energy allowed the discrimination between normal and dysphonic voice. Furthermore, this technique could discriminate the voice signal of patients suffering from left vocal fold paralysis from those suffering from other investigated disorders. The results suggest the global energy of the signal as an auxiliary and alternative tool for the diagnosis between normal and dysphonic voice.

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“…To analyse low-frequency vocal modulation, the phonatory frequency estimator must be able to track small frequency perturbations and handle disordered speech signals. Algorithms that estimate the phonatory frequency (Hess, 1983;Mitev and Hadjitodorov, 2003) fall into different categories, which involve the measurement of the length of each vocal cycle (Kadambe and Boudreaux-Bartels, 1992;Schoentgen, 2002), the estimation of the average period in an analysis frame (Medan et al, 1991;Boersma and Weenink, 2004), or the estimation of the instantaneous frequency of the fundamental spectral component of the speech signal (Winholtz and Ramig, 1992). The last category presents advantages when tracking small frequency perturbations: Firstly, the phonatory frequency must not be considered stationary over an analysis frame.…”

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confidence: 99%

Low-frequency vocal modulations in vowels produced by Parkinsonian subjects

Cnockaert

Schoentgen

Auzou

et al. 2008

Speech Communication

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Low-frequency vocal modulations here designate slow disturbances of the phonatory frequency F 0. They are present in all voiced speech sounds, but their properties may be affected by neurological disease. An analysis method, based on continuous wavelet transforms, is proposed to extract the phonatory frequency trace and lowfrequency vocal modulation in sustained speech sounds. The method is used to analyze a corpus of vowels uttered by male and female speakers, some of whom are healthy and some of whom suffer from Parkinson's disease. The latter present general speech problems but their voice is not perceived as tremulous. The objective is to discover differences between speaker groups in F 0 low-frequency modulations. Results show that Parkinson's disease has different effects on the voice of male and female speakers. The average phonatory frequency is significantly higher for male parkinsonian speakers. The modulation amplitude is significantly higher for female parkinsonian speakers. The modulation frequency is significantly higher and the ratio between the modulation energies in the frequency-bands [3Hz, 7Hz] and [7Hz, 15Hz] is significantly lower for parkinsonian speakers of both genders.

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Fundamental frequency estimation of voice of patients with laryngeal disorders

Cited by 23 publications

References 18 publications

Fundamental Frequency Estimation of Low-quality Electroglottographic Signals

Fundamental Frequency Estimation of Low-quality Electroglottographic Signals

Assessment of laryngeal disorders through the global energy of speech

Low-frequency vocal modulations in vowels produced by Parkinsonian subjects

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