Chaos in Voice, From Modeling to Measurement

Jiang, Jack J.; Zhang, Yu; McGilligan, Clancy

doi:10.1016/j.jvoice.2005.01.001

Cited by 159 publications

(146 citation statements)

References 67 publications

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“…[8][9][10][11][12][19][20][21][22] These types of measures have complemented traditional perturbation measurements due to an ability to describe chaotic, aperiodic voices. [8][9]11 A reconstructed phase space can be created by plotting a voice signal against itself at some time delay. The reconstructed phase space qualitatively shows the dynamic behavior of a signal, as a periodic signal produces a closed trajectory while an aperiodic signal appears irregular.…”

Section: Nonlinear Dynamic Analysismentioning

confidence: 99%

“…The concept of human voice production as a chaotic system has been established in recent years through computer modeling, excised larynx experiments, and human voice analysis. [8][9][10][11][12] A chaotic voice often exhibits an irregular and aperiodic waveform, poor perceptual qualities, and extreme perturbation values. Because all human vocal folds exhibit some inherent chaotic properties, nonlinear dynamic methods are useful for quantifying the degree of aperiodicity and irregularity.…”

Section: Introductionmentioning

confidence: 99%

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Acoustic Analysis of Aperiodic Voice: Perturbation and Nonlinear Dynamic Properties in Esophageal Phonation

et al. 2009

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Objectives/Hypothesis-Esophageal voice is a method of communication after total laryngectomy. Previous research suggests that perturbation analysis may inaccurately measure aperiodic voices and that nonlinear dynamic methods may be more appropriate for analyzing signals of this type. Therefore, we hypothesized that nonlinear dynamic analysis would be more capable than perturbation parameters for reliable measurement of the aperiodic esophageal voice.Study Design-Acoustic comparison of esophageal and normal voice cohorts using nonlinear dynamic and perturbation measures.Methods-Twenty subjects in two age-matched groups participated in the study. Jitter, shimmer, signal-to-noise ratio, correlation dimension, and second-order entropy were measured from audio recordings of subjects' voices.Results-Jitter and shimmer values were significantly higher for esophageal voices and signalto-noise ratio values were significantly lower for esophageal voices than for normal voices. Error count values, which indicate perturbation analysis reliability, were 0 in normal voices and significantly higher in esophageal voices. Error was attributable to signal aperiodicity and demonstrated that perturbation analysis yielded questionable results for esophageal voice. However, nonlinear dynamics measures analyzed both cohorts reliably and indicated that esophageal voice was significantly more chaotic than normal voice. Conclusions-The results demonstrated the capability of nonlinear dynamic methods to reliably quantify both aperiodic and periodic signals and to differentiate normal from esophageal voices. It is suggested that nonlinear dynamic analysis be used preferentially for acoustic characterization of aperiodic voices such as esophageal voice. Future research should focus on clarification of perturbation analysis reliability and further application of nonlinear dynamic measures to aperiodic voices.

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Section: Nonlinear Dynamic Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Acoustic Analysis of Aperiodic Voice: Perturbation and Nonlinear Dynamic Properties in Esophageal Phonation

et al. 2009

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“…Despotovic et al studied a variety of nonlinear Volterra prediction models of chaotic time series [22,23]. Subsequently, many scholars proposed the application of various algorithms such as least mean square, normalized least mean square, recursive least square, and other algorithms in the identification of Volterra model system [24][25][26][27]. Tian and Liu [28] analyzed the sampling signal of ocean echo in time domain of high-frequency radar and verified that it not only has chaotic characteristics but also has fractal characteristics.…”

Section: Introductionmentioning

confidence: 99%

Detection of Low‐Flying Target under the Sea Clutter Background Based on Volterra Filter

Xing

Yan

2018

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In order to detect low-flying small targets in complex sea condition effectively, we study the chaotic characteristic of sea clutter, use joint algorithm combined complete ensemble empirical mode decomposition (CEEMD) with wavelet transform to de-noise, and put forward a detection method for low-flying target under the sea clutter background based on Volterra filter. By CEEMD method, sea clutter signal which contains small target can be decomposed into a series of intrinsic mode function (IMF) components, pick out high-frequency components which contain more noise by autocorrelation function, and perform wavelet transform on them. The de-noised components and remaining components are used to reconstruct clear signal. In view of the chaotic characteristics of sea clutter, we use Volterra filter to establish adaptive prediction model, detect low-flying small target hiding in sea clutter background from the prediction error, and compare the root mean square error (RMSE) before and after de-noising to evaluate de-noising effect. Experimental results show that the joint algorithm can effectively remove noise and reduce the RMSE by 40% at least. Volterra prediction model can directly detect low-flying small target under sea clutter background from the prediction error in the cases of high signal-to-noise ratio (SNR). In the cases of low SNR, after de-noised by joint algorithm, Volterra prediction model can also detect the low-flying small target clearly.

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“…Apesar do relativo sucesso do uso do modelo linear para a produção da fala em diversas aplicações, estudos mais recentes têm apontado para a evidência do caos na voz humana (Jiang et al, 2006;Kokkinos e Maragos, 2005;Henríquez et al, 2009;Zhang e Jiang, 2008). A análise dinâmica não linear de sinais de voz tem sido considerada por levar em conta aspectos da voz humana, não explorada na abordagem linear, tais como: variação temporal da forma do trato vocal, as ressonâncias associadas à sua fisiologia, as perdas devido ao atrito viscoso nas paredes internas do trato vocal, a suavidade dessas paredes internas, a radiação do som nos lábios, o acoplamento nasal e a flexibilidade (comportamento dinâmico) associada à vibração das pregas vocais (Kumar e Mullik, 1996).…”

Section: Introductionunclassified

“…Recentes pesquisas relacionadas às séries temporais, geradas a partir dos mecanismos de produção da voz humana, têm sido realizadas considerando-se as técnicas da dinâmica não linear e da teoria do caos com objetivos variados, dentre os quais podem ser destacados: classificação de fonemas (Johnson et al, 2005;Kokkinos e Maragos, 2005), reconhecimento automático de locutor (Petry, 2002), discriminação entre vozes saudáveis e patológicas, diagnóstico de patologias laríngeas e avaliação de efeitos de tratamentos clínicos (Dajer, 2006;Henríquez et al, 2009;Jiang et al, 2006;Scalassara et al, 2008;Torres et al, 2003;Zhang e Jiang, 2008).…”

Section: Introductionunclassified

Classificação de sinais de vozes saudáveis e patológicas por meio da combinação entre medidas da análise dinâmica não linear e codificação preditiva linear

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et al. 2013

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Chaos in Voice, From Modeling to Measurement

Cited by 159 publications

References 67 publications

Acoustic Analysis of Aperiodic Voice: Perturbation and Nonlinear Dynamic Properties in Esophageal Phonation

Acoustic Analysis of Aperiodic Voice: Perturbation and Nonlinear Dynamic Properties in Esophageal Phonation

Detection of Low‐Flying Target under the Sea Clutter Background Based on Volterra Filter

Classificação de sinais de vozes saudáveis e patológicas por meio da combinação entre medidas da análise dinâmica não linear e codificação preditiva linear

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