The Experts below are selected from a list of 6849 Experts worldwide ranked by ideXlab platform
Rubens Sampaio - One of the best experts on this subject based on the ideXlab platform.
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Uncertainty quantification of Voice Signal production mechanical model and experimental updating
Mechanical Systems and Signal Processing, 2013Co-Authors: Edson Cataldo, Christian Soize, Rubens SampaioAbstract:The aim of this paper is to analyze the uncertainty quantification in a Voice production mechanical model and update the probability density function corresponding to the tension parameter using the Bayes method and experimental data. Three parameters are considered uncertain in the Voice production mechanical model used: the tension parameter, the neutral glottal area and the subglottal pressure. The tension parameter of the vocal folds is mainly responsible for the changing of the fundamental frequency of a Voice Signal, generated by a mechanical/mathematical model for producing Voiced sounds. The three uncertain parameters are modeled by random variables. The probability density function related to the tension parameter is considered uniform and the probability density functions related to the neutral glottal area and the subglottal pressure are constructed using the Maximum Entropy Principle. The output of the stochastic computational model is the random Voice Signal and the Monte Carlo method is used to solve the stochastic equations allowing realizations of the random Voice Signals to be generated. For each realization of the random Voice Signal, the corresponding realization of the random fundamental frequency is calculated and the prior pdf of this random fundamental frequency is then estimated. Experimental data are available for the fundamental frequency and the posterior probability density function of the random tension parameter is then estimated using the Bayes method. In addition, an application is performed considering a case with a pathology in the vocal folds. The strategy developed here is important mainly due to two things. The first one is related to the possibility of updating the probability density function of a parameter, the tension parameter of the vocal folds, which cannot be measured direct and the second one is related to the construction of the likelihood function. In general, it is predefined using the known pdf. Here, it is constructed in a new and different manner, using the own system considered.
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Uncertainty quantification of Voice Signal production mechanical model and experimental updating
Mechanical Systems and Signal Processing, 2013Co-Authors: Edson Cataldo, Christian Soize, Rubens SampaioAbstract:The aim of this paper is to analyze the uncertainty quantification in a Voice production mechanical model and update the probability density function corresponding to the tension parameter using the bayes method and experimental data. Three parameters are considered uncertain in the Voice production mechanical model used: the tension parameter, the neutral glottal area and the subglottal pressure. The tension parameter of the vocal folds is mainly responsible for the changing of the fundamental frequency of a Voice Signal, generated by a mechanical/mathematical model for producing Voiced sounds. The three uncertain parameters are modeled by random variables. Experimental data are available for the fundamental frequency and the posterior probability density function of the random tension parameter is then estimated using the Bayes method. In addition, an application is performed considering a case with a pathology in the vocal folds.
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updating the probabilistic density function related to an uncertain parameter of a model for producing Voice using bayesian approach
Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS 2012), 2012Co-Authors: Edson Cataldo, Christian Soize, Rubens SampaioAbstract:The aim of this paper is to use the Bayesian method for updating a probability density function (pdf) related to the tension parameter of the vocal folds. This parameter is mainly responsible for the changing of the fundamental frequency of a Voice Signal, generated by a mechanical/mathematical model for producing Voiced sounds. Three parameters are considered uncertain in the model used: the tension parameter, the neutral glottal area and the subglottal pressure. These uncertain parameters are modeled by random variables and their prior probability density functions are constructed using the Maximum Entropy Principle. The output of the stochastic computational model is the random Voice Signal and the Monte Carlo method is used to solve the stochastic equations allowing realizations of the random Voice Signals to be generated. Experimental data are available for the fundamental frequency and the posterior probability density function of the random tension parameter is then estimated using the Bayes method.
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using bayesian method for updating the probability density function related to the tension parameter in a Voice production model
Journal of Biomechanics, 2012Co-Authors: Edson Cataldo, Christian Soize, Rubens SampaioAbstract:The aim of this paper is to use the Bayesian method for updating a probability density function (pdf) related to the tension parameter of the vocal folds. This parameter is mainly responsible for the changing of the fundamental frequency of a Voice Signal, generated by a mechanical/mathematical model for producing Voiced sounds. Three parameters are considered uncertain in the model used: the tension parameter, the neutral glottal area and the subglottal pressure. These uncertain parameters are modeled by random variables and their prior probability density functions are constructed using the Maximum Entropy Principle. The output of the stochastic computational model is the random Voice Signal and the Monte Carlo method is used to solve the stochastic equations allowing realizations of the random Voice Signals to be generated.
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a computational method for updating a probabilistic model of an uncertain parameter in a Voice production model
1st International Symposium on Uncertainty Quantification and Stochastic Modeling (Uncertainties 2012), 2012Co-Authors: Edson Cataldo, Christian Soize, Rubens SampaioAbstract:The aim of this paper is to use Bayesian statistics to update a probability density function (p.d.f.) related to the tension parameter of the vocal folds, which is one of the main parameters responsible for the changing of the fundamental frequency of a Voice Signal, generated by a mechanical - mathematical model for producing Voiced sounds. Three parameters are considered uncertain in the model used: the tension parameter, the neutral glottal area and the subglottal pressure. Random variables are associated to the uncertain parameters and their corresponding p.d.f.'s are constructed using the Maximum Entropy Principle. The Monte Carlo method is used to generate the Voice Signals, which are the outputs of the model. For each Voice Signal, the corresponding fundamental frequency is calculated and a p.d.f. for this random variable is constructed. Experimental values of the fundamental frequency are then used to update the p.d.f. of the fundamental frequency and, consequently, of the tension parameter, through Bayes' method.
Edson Cataldo - One of the best experts on this subject based on the ideXlab platform.
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Uncertainty quantification of Voice Signal production mechanical model and experimental updating
Mechanical Systems and Signal Processing, 2013Co-Authors: Edson Cataldo, Christian Soize, Rubens SampaioAbstract:The aim of this paper is to analyze the uncertainty quantification in a Voice production mechanical model and update the probability density function corresponding to the tension parameter using the Bayes method and experimental data. Three parameters are considered uncertain in the Voice production mechanical model used: the tension parameter, the neutral glottal area and the subglottal pressure. The tension parameter of the vocal folds is mainly responsible for the changing of the fundamental frequency of a Voice Signal, generated by a mechanical/mathematical model for producing Voiced sounds. The three uncertain parameters are modeled by random variables. The probability density function related to the tension parameter is considered uniform and the probability density functions related to the neutral glottal area and the subglottal pressure are constructed using the Maximum Entropy Principle. The output of the stochastic computational model is the random Voice Signal and the Monte Carlo method is used to solve the stochastic equations allowing realizations of the random Voice Signals to be generated. For each realization of the random Voice Signal, the corresponding realization of the random fundamental frequency is calculated and the prior pdf of this random fundamental frequency is then estimated. Experimental data are available for the fundamental frequency and the posterior probability density function of the random tension parameter is then estimated using the Bayes method. In addition, an application is performed considering a case with a pathology in the vocal folds. The strategy developed here is important mainly due to two things. The first one is related to the possibility of updating the probability density function of a parameter, the tension parameter of the vocal folds, which cannot be measured direct and the second one is related to the construction of the likelihood function. In general, it is predefined using the known pdf. Here, it is constructed in a new and different manner, using the own system considered.
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Uncertainty quantification of Voice Signal production mechanical model and experimental updating
Mechanical Systems and Signal Processing, 2013Co-Authors: Edson Cataldo, Christian Soize, Rubens SampaioAbstract:The aim of this paper is to analyze the uncertainty quantification in a Voice production mechanical model and update the probability density function corresponding to the tension parameter using the bayes method and experimental data. Three parameters are considered uncertain in the Voice production mechanical model used: the tension parameter, the neutral glottal area and the subglottal pressure. The tension parameter of the vocal folds is mainly responsible for the changing of the fundamental frequency of a Voice Signal, generated by a mechanical/mathematical model for producing Voiced sounds. The three uncertain parameters are modeled by random variables. Experimental data are available for the fundamental frequency and the posterior probability density function of the random tension parameter is then estimated using the Bayes method. In addition, an application is performed considering a case with a pathology in the vocal folds.
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updating the probabilistic density function related to an uncertain parameter of a model for producing Voice using bayesian approach
Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS 2012), 2012Co-Authors: Edson Cataldo, Christian Soize, Rubens SampaioAbstract:The aim of this paper is to use the Bayesian method for updating a probability density function (pdf) related to the tension parameter of the vocal folds. This parameter is mainly responsible for the changing of the fundamental frequency of a Voice Signal, generated by a mechanical/mathematical model for producing Voiced sounds. Three parameters are considered uncertain in the model used: the tension parameter, the neutral glottal area and the subglottal pressure. These uncertain parameters are modeled by random variables and their prior probability density functions are constructed using the Maximum Entropy Principle. The output of the stochastic computational model is the random Voice Signal and the Monte Carlo method is used to solve the stochastic equations allowing realizations of the random Voice Signals to be generated. Experimental data are available for the fundamental frequency and the posterior probability density function of the random tension parameter is then estimated using the Bayes method.
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using bayesian method for updating the probability density function related to the tension parameter in a Voice production model
Journal of Biomechanics, 2012Co-Authors: Edson Cataldo, Christian Soize, Rubens SampaioAbstract:The aim of this paper is to use the Bayesian method for updating a probability density function (pdf) related to the tension parameter of the vocal folds. This parameter is mainly responsible for the changing of the fundamental frequency of a Voice Signal, generated by a mechanical/mathematical model for producing Voiced sounds. Three parameters are considered uncertain in the model used: the tension parameter, the neutral glottal area and the subglottal pressure. These uncertain parameters are modeled by random variables and their prior probability density functions are constructed using the Maximum Entropy Principle. The output of the stochastic computational model is the random Voice Signal and the Monte Carlo method is used to solve the stochastic equations allowing realizations of the random Voice Signals to be generated.
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a computational method for updating a probabilistic model of an uncertain parameter in a Voice production model
1st International Symposium on Uncertainty Quantification and Stochastic Modeling (Uncertainties 2012), 2012Co-Authors: Edson Cataldo, Christian Soize, Rubens SampaioAbstract:The aim of this paper is to use Bayesian statistics to update a probability density function (p.d.f.) related to the tension parameter of the vocal folds, which is one of the main parameters responsible for the changing of the fundamental frequency of a Voice Signal, generated by a mechanical - mathematical model for producing Voiced sounds. Three parameters are considered uncertain in the model used: the tension parameter, the neutral glottal area and the subglottal pressure. Random variables are associated to the uncertain parameters and their corresponding p.d.f.'s are constructed using the Maximum Entropy Principle. The Monte Carlo method is used to generate the Voice Signals, which are the outputs of the model. For each Voice Signal, the corresponding fundamental frequency is calculated and a p.d.f. for this random variable is constructed. Experimental values of the fundamental frequency are then used to update the p.d.f. of the fundamental frequency and, consequently, of the tension parameter, through Bayes' method.
Mads Græsbøll Christensen - One of the best experts on this subject based on the ideXlab platform.
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dominant distortion classification for pre processing of vowels in remote biomedical Voice analysis
Conference of the International Speech Communication Association, 2017Co-Authors: Amir Hossein Poorjam, Jesper Jensen, Max A. Little, Mads Græsbøll ChristensenAbstract:Advances in speech Signal analysis facilitate the development of techniques for remote biomedical Voice assessment. However, the performance of these techniques is affected by noise and distortion in Signals. In this paper, we focus on the vowel /a/ as the most widely-used Voice Signal for pathological Voice assessments and investigate the impact of four major types of distortion that are commonly present during recording or transmission in Voice analysis, namely: background noise, reverberation, clipping and compression, on Mel-frequency cepstral coefficients (MFCCs) - the most widely-used features in biomedical Voice analysis. Then, we propose a new distortion classification approach to detect the most dominant distortion in such Voice Signals. The proposed method involves MFCCs as frame-level features and a support vector machine as classifier to detect the presence and type of distortion in frames of a given Voice Signal. Experimental results obtained from the healthy and Parkinson's Voices show the effectiveness of the proposed approach in distortion detection and classification.
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INTERSPEECH - Dominant distortion classification for pre-processing of vowels in remote biomedical Voice analysis
Interspeech 2017, 2017Co-Authors: Amir Hossein Poorjam, Jesper Jensen, Max A. Little, Mads Græsbøll ChristensenAbstract:Advances in speech Signal analysis facilitate the development of techniques for remote biomedical Voice assessment. However, the performance of these techniques is affected by noise and distortion in Signals. In this paper, we focus on the vowel /a/ as the most widely-used Voice Signal for pathological Voice assessments and investigate the impact of four major types of distortion that are commonly present during recording or transmission in Voice analysis, namely: background noise, reverberation, clipping and compression, on Mel-frequency cepstral coefficients (MFCCs) - the most widely-used features in biomedical Voice analysis. Then, we propose a new distortion classification approach to detect the most dominant distortion in such Voice Signals. The proposed method involves MFCCs as frame-level features and a support vector machine as classifier to detect the presence and type of distortion in frames of a given Voice Signal. Experimental results obtained from the healthy and Parkinson's Voices show the effectiveness of the proposed approach in distortion detection and classification.
Max A. Little - One of the best experts on this subject based on the ideXlab platform.
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dominant distortion classification for pre processing of vowels in remote biomedical Voice analysis
Conference of the International Speech Communication Association, 2017Co-Authors: Amir Hossein Poorjam, Jesper Jensen, Max A. Little, Mads Græsbøll ChristensenAbstract:Advances in speech Signal analysis facilitate the development of techniques for remote biomedical Voice assessment. However, the performance of these techniques is affected by noise and distortion in Signals. In this paper, we focus on the vowel /a/ as the most widely-used Voice Signal for pathological Voice assessments and investigate the impact of four major types of distortion that are commonly present during recording or transmission in Voice analysis, namely: background noise, reverberation, clipping and compression, on Mel-frequency cepstral coefficients (MFCCs) - the most widely-used features in biomedical Voice analysis. Then, we propose a new distortion classification approach to detect the most dominant distortion in such Voice Signals. The proposed method involves MFCCs as frame-level features and a support vector machine as classifier to detect the presence and type of distortion in frames of a given Voice Signal. Experimental results obtained from the healthy and Parkinson's Voices show the effectiveness of the proposed approach in distortion detection and classification.
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INTERSPEECH - Dominant distortion classification for pre-processing of vowels in remote biomedical Voice analysis
Interspeech 2017, 2017Co-Authors: Amir Hossein Poorjam, Jesper Jensen, Max A. Little, Mads Græsbøll ChristensenAbstract:Advances in speech Signal analysis facilitate the development of techniques for remote biomedical Voice assessment. However, the performance of these techniques is affected by noise and distortion in Signals. In this paper, we focus on the vowel /a/ as the most widely-used Voice Signal for pathological Voice assessments and investigate the impact of four major types of distortion that are commonly present during recording or transmission in Voice analysis, namely: background noise, reverberation, clipping and compression, on Mel-frequency cepstral coefficients (MFCCs) - the most widely-used features in biomedical Voice analysis. Then, we propose a new distortion classification approach to detect the most dominant distortion in such Voice Signals. The proposed method involves MFCCs as frame-level features and a support vector machine as classifier to detect the presence and type of distortion in frames of a given Voice Signal. Experimental results obtained from the healthy and Parkinson's Voices show the effectiveness of the proposed approach in distortion detection and classification.
Christian Soize - One of the best experts on this subject based on the ideXlab platform.
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Uncertainty quantification of Voice Signal production mechanical model and experimental updating
Mechanical Systems and Signal Processing, 2013Co-Authors: Edson Cataldo, Christian Soize, Rubens SampaioAbstract:The aim of this paper is to analyze the uncertainty quantification in a Voice production mechanical model and update the probability density function corresponding to the tension parameter using the Bayes method and experimental data. Three parameters are considered uncertain in the Voice production mechanical model used: the tension parameter, the neutral glottal area and the subglottal pressure. The tension parameter of the vocal folds is mainly responsible for the changing of the fundamental frequency of a Voice Signal, generated by a mechanical/mathematical model for producing Voiced sounds. The three uncertain parameters are modeled by random variables. The probability density function related to the tension parameter is considered uniform and the probability density functions related to the neutral glottal area and the subglottal pressure are constructed using the Maximum Entropy Principle. The output of the stochastic computational model is the random Voice Signal and the Monte Carlo method is used to solve the stochastic equations allowing realizations of the random Voice Signals to be generated. For each realization of the random Voice Signal, the corresponding realization of the random fundamental frequency is calculated and the prior pdf of this random fundamental frequency is then estimated. Experimental data are available for the fundamental frequency and the posterior probability density function of the random tension parameter is then estimated using the Bayes method. In addition, an application is performed considering a case with a pathology in the vocal folds. The strategy developed here is important mainly due to two things. The first one is related to the possibility of updating the probability density function of a parameter, the tension parameter of the vocal folds, which cannot be measured direct and the second one is related to the construction of the likelihood function. In general, it is predefined using the known pdf. Here, it is constructed in a new and different manner, using the own system considered.
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Uncertainty quantification of Voice Signal production mechanical model and experimental updating
Mechanical Systems and Signal Processing, 2013Co-Authors: Edson Cataldo, Christian Soize, Rubens SampaioAbstract:The aim of this paper is to analyze the uncertainty quantification in a Voice production mechanical model and update the probability density function corresponding to the tension parameter using the bayes method and experimental data. Three parameters are considered uncertain in the Voice production mechanical model used: the tension parameter, the neutral glottal area and the subglottal pressure. The tension parameter of the vocal folds is mainly responsible for the changing of the fundamental frequency of a Voice Signal, generated by a mechanical/mathematical model for producing Voiced sounds. The three uncertain parameters are modeled by random variables. Experimental data are available for the fundamental frequency and the posterior probability density function of the random tension parameter is then estimated using the Bayes method. In addition, an application is performed considering a case with a pathology in the vocal folds.
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updating the probabilistic density function related to an uncertain parameter of a model for producing Voice using bayesian approach
Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS 2012), 2012Co-Authors: Edson Cataldo, Christian Soize, Rubens SampaioAbstract:The aim of this paper is to use the Bayesian method for updating a probability density function (pdf) related to the tension parameter of the vocal folds. This parameter is mainly responsible for the changing of the fundamental frequency of a Voice Signal, generated by a mechanical/mathematical model for producing Voiced sounds. Three parameters are considered uncertain in the model used: the tension parameter, the neutral glottal area and the subglottal pressure. These uncertain parameters are modeled by random variables and their prior probability density functions are constructed using the Maximum Entropy Principle. The output of the stochastic computational model is the random Voice Signal and the Monte Carlo method is used to solve the stochastic equations allowing realizations of the random Voice Signals to be generated. Experimental data are available for the fundamental frequency and the posterior probability density function of the random tension parameter is then estimated using the Bayes method.
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using bayesian method for updating the probability density function related to the tension parameter in a Voice production model
Journal of Biomechanics, 2012Co-Authors: Edson Cataldo, Christian Soize, Rubens SampaioAbstract:The aim of this paper is to use the Bayesian method for updating a probability density function (pdf) related to the tension parameter of the vocal folds. This parameter is mainly responsible for the changing of the fundamental frequency of a Voice Signal, generated by a mechanical/mathematical model for producing Voiced sounds. Three parameters are considered uncertain in the model used: the tension parameter, the neutral glottal area and the subglottal pressure. These uncertain parameters are modeled by random variables and their prior probability density functions are constructed using the Maximum Entropy Principle. The output of the stochastic computational model is the random Voice Signal and the Monte Carlo method is used to solve the stochastic equations allowing realizations of the random Voice Signals to be generated.
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a computational method for updating a probabilistic model of an uncertain parameter in a Voice production model
1st International Symposium on Uncertainty Quantification and Stochastic Modeling (Uncertainties 2012), 2012Co-Authors: Edson Cataldo, Christian Soize, Rubens SampaioAbstract:The aim of this paper is to use Bayesian statistics to update a probability density function (p.d.f.) related to the tension parameter of the vocal folds, which is one of the main parameters responsible for the changing of the fundamental frequency of a Voice Signal, generated by a mechanical - mathematical model for producing Voiced sounds. Three parameters are considered uncertain in the model used: the tension parameter, the neutral glottal area and the subglottal pressure. Random variables are associated to the uncertain parameters and their corresponding p.d.f.'s are constructed using the Maximum Entropy Principle. The Monte Carlo method is used to generate the Voice Signals, which are the outputs of the model. For each Voice Signal, the corresponding fundamental frequency is calculated and a p.d.f. for this random variable is constructed. Experimental values of the fundamental frequency are then used to update the p.d.f. of the fundamental frequency and, consequently, of the tension parameter, through Bayes' method.
