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Papers by Julian Arias

Research paper thumbnail of USO DE INMUNOGLOBULlNA EN EL MANEJO DEL SINDROME DE STEVENS-JOHNSON. REPORTE DE 1 CASO

Research paper thumbnail of Automatic Detection of Hypernasality in Children

Automatic hypernasality detection in children with Cleft Lip and Palate is made considering five ... more Automatic hypernasality detection in children with Cleft Lip and Palate is made considering five Spanish vowels. Characterization is performed by means of some acoustic and noise features, building a representation space with high dimensionality. Most relevant features are selected using Principal Components Analisis and linear correlation in order to enable clinical interpretation of results and achieving spaces with lower dimensions per vowel. Using a Linear-Bayes classifier, success rates between 80% and 90% are reached, beating success rates achived in similiar studies recently reported.

Research paper thumbnail of DISEÑO SIMULTÁNEO DE UNA ETAPA DE EXTRACCIÓN DE CARACTERÍSTICAS Y UN CLASIFICADOR BASADO EN HMM

Se presenta una metodología de diseño simultáneo de una etapa de extracción de características y ... more Se presenta una metodología de diseño simultáneo de una etapa de extracción de características y un clasificador basado en modelos ocultos de Markov (HMM), por medio del algoritmo de mínimo error de clasificación (MCE). La extracción de características ...

Research paper thumbnail of Effects of Audio Compression in Automatic Detection of Voice Pathologies

IEEE Transactions on Biomedical Engineering, 2008

Research paper thumbnail of Dysphonia detection based on modulation spectral features and cepstral coefficients

Research paper thumbnail of Complexity analysis of pathological voices by means of hidden markov entropy measurements

In this work an entropy based nonlinear analysis of pathological voices is presented. The complex... more In this work an entropy based nonlinear analysis of pathological voices is presented. The complexity analysis is carried out by means of six different entropies, including three measures derived from the entropy rate of Markov chains. The aim is to characterize the divergence of the trajectories and theirs directions into the state space of Markov chains. By employing these measures in conjunction with conventional entropy features, it is possible to improve the discrimination capabilities of the nonlinear analysis in the automatic detection of pathological voices.

Research paper thumbnail of Towards collaborative work among speech therapists, phoniatricians, and ENT professionals: Analysis of the impact of ciphering techniques in the performance of an integrated tool for the diagnosis of voice disorders

Biomedical Signal Processing and Control

Research paper thumbnail of An improved method for voice pathology detection by means of a HMM-based feature space transformation

Pattern Recognition, 2010

Research paper thumbnail of Automatic Detection of Pathological Voices Using Complexity Measures, Noise Parameters, and Mel-Cepstral Coefficients

IEEE Transactions on Biomedical Engineering, 2011

This paper proposes a new approach to improve the amount of information extracted from the speech... more This paper proposes a new approach to improve the amount of information extracted from the speech aiming to increase the accuracy of a system developed for the automatic detection of pathological voices. The paper addresses the discrimination capabilities of 11 features extracted using nonlinear analysis of time series. Two of these features are based on conventional nonlinear statistics (largest Lyapunov exponent and correlation dimension), two are based on recurrence and fractal-scaling analysis, and the remaining are based on different estimations of the entropy. Moreover, this paper uses a strategy based on combining classifiers for fusing the nonlinear analysis with the information provided by classic parameterization approaches found in the literature (noise parameters and mel-frequency cepstral coefficients). The classification was carried out in two steps using, first, a generative and, later, a discriminative approach. Combining both classifiers, the best accuracy obtained is 98.23% ± 0.001.

Research paper thumbnail of USO DE INMUNOGLOBULlNA EN EL MANEJO DEL SINDROME DE STEVENS-JOHNSON. REPORTE DE 1 CASO

Research paper thumbnail of Automatic Detection of Hypernasality in Children

Automatic hypernasality detection in children with Cleft Lip and Palate is made considering five ... more Automatic hypernasality detection in children with Cleft Lip and Palate is made considering five Spanish vowels. Characterization is performed by means of some acoustic and noise features, building a representation space with high dimensionality. Most relevant features are selected using Principal Components Analisis and linear correlation in order to enable clinical interpretation of results and achieving spaces with lower dimensions per vowel. Using a Linear-Bayes classifier, success rates between 80% and 90% are reached, beating success rates achived in similiar studies recently reported.

Research paper thumbnail of DISEÑO SIMULTÁNEO DE UNA ETAPA DE EXTRACCIÓN DE CARACTERÍSTICAS Y UN CLASIFICADOR BASADO EN HMM

Se presenta una metodología de diseño simultáneo de una etapa de extracción de características y ... more Se presenta una metodología de diseño simultáneo de una etapa de extracción de características y un clasificador basado en modelos ocultos de Markov (HMM), por medio del algoritmo de mínimo error de clasificación (MCE). La extracción de características ...

Research paper thumbnail of Effects of Audio Compression in Automatic Detection of Voice Pathologies

IEEE Transactions on Biomedical Engineering, 2008

Research paper thumbnail of Dysphonia detection based on modulation spectral features and cepstral coefficients

Research paper thumbnail of Complexity analysis of pathological voices by means of hidden markov entropy measurements

In this work an entropy based nonlinear analysis of pathological voices is presented. The complex... more In this work an entropy based nonlinear analysis of pathological voices is presented. The complexity analysis is carried out by means of six different entropies, including three measures derived from the entropy rate of Markov chains. The aim is to characterize the divergence of the trajectories and theirs directions into the state space of Markov chains. By employing these measures in conjunction with conventional entropy features, it is possible to improve the discrimination capabilities of the nonlinear analysis in the automatic detection of pathological voices.

Research paper thumbnail of Towards collaborative work among speech therapists, phoniatricians, and ENT professionals: Analysis of the impact of ciphering techniques in the performance of an integrated tool for the diagnosis of voice disorders

Biomedical Signal Processing and Control

Research paper thumbnail of An improved method for voice pathology detection by means of a HMM-based feature space transformation

Pattern Recognition, 2010

Research paper thumbnail of Automatic Detection of Pathological Voices Using Complexity Measures, Noise Parameters, and Mel-Cepstral Coefficients

IEEE Transactions on Biomedical Engineering, 2011

This paper proposes a new approach to improve the amount of information extracted from the speech... more This paper proposes a new approach to improve the amount of information extracted from the speech aiming to increase the accuracy of a system developed for the automatic detection of pathological voices. The paper addresses the discrimination capabilities of 11 features extracted using nonlinear analysis of time series. Two of these features are based on conventional nonlinear statistics (largest Lyapunov exponent and correlation dimension), two are based on recurrence and fractal-scaling analysis, and the remaining are based on different estimations of the entropy. Moreover, this paper uses a strategy based on combining classifiers for fusing the nonlinear analysis with the information provided by classic parameterization approaches found in the literature (noise parameters and mel-frequency cepstral coefficients). The classification was carried out in two steps using, first, a generative and, later, a discriminative approach. Combining both classifiers, the best accuracy obtained is 98.23% ± 0.001.

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