María Eugenia Torres - Academia.edu (original) (raw)

Papers by María Eugenia Torres

This paper deals with a particular aspect of the relation between signal analysis and nonlinear d... more This paper deals with a particular aspect of the relation between signal analysis and nonlinear dynamics, which is the detection of changes in parameters of nonlinear dynamical systems from the information obtained using experimental data. We propose a combination of the multiresolution wavelet analysis with the idea of entropy. We show that it provides a new tool, that we calle multiresolution entropy (MRE), useful for analyzing nonstationary signals and for the detection and localization of slight changes in nonlinear ...

The present work evaluates the intelligibility and the quality of speech signals after being proc... more The present work evaluates the intelligibility and the quality of speech signals after being processed using a group of noise reduction techniques. The intelligibility is measured in percentage of correctly recognized words in a subjective test, and the most frequent phonetic sustitutions are discussed in terms of confusion matrices. The quality of the obtained signals is evaluated also in an objective way starting from a group of measures selected ad-hoc. In this work the evaluation is presented for some of the most commonly employed classic techniques, such as Spectral Subtraction, Wiener and Ephraim-Malah filtering. The results of the preliminary evaluation of more recent techniques are also shown, as the ones based in the Wavelet transform. The relative performance of each considered algorithm is presented and discussed.

In this paper an algorithm based on the ensemble empirical mode decomposition (EEMD) is presented... more In this paper an algorithm based on the ensemble empirical mode decomposition (EEMD) is presented. The key idea on the EEMD relies on averaging the modes obtained by EMD applied to several realizations of Gaussian white noise added to the original signal. The resulting decomposition solves the EMD mode mixing problem, however it introduces new ones. In the method here proposed, a particular noise is added at each stage of the decomposition and a unique residue is computed to obtain each mode. The resulting decomposition is complete, with a numerically negligible error. Two examples are presented: a discrete Dirac delta function and an electrocardiogram signal. The results show that, compared with EEMD, the new method here presented also provides a better spectral separation of the modes and a lesser number of sifting iterations is needed, reducing the computational cost.

Physica A: Statistical Mechanics and its Applications, 2016

Multifractal analysis studies signals, functions, images or fields via the fluctuations of their ... more Multifractal analysis studies signals, functions, images or fields via the fluctuations of their local regularity along time or space, which capture crucial features of their temporal/spatial dynamics. It has become a standard signal and image processing tool and is commonly used in numerous applications of different natures. In its common formulation, it relies on the Hölder exponent as a measure of local regularity, which is by nature restricted to positive values and can hence be used for locally bounded functions only. In this contribution, it is proposed to replace the Hölder exponent with a collection of novel exponents for measuring local regularity, the p-exponents. One of the major virtues of p-exponents is that they can potentially take negative values. The corresponding wavelet-based multiscale quantities, the p-leaders, are constructed and shown to permit the definition of a new multifractal formalism, yielding an accurate practical estimation of the multifractal properties of real-world data. Moreover, theoretical and practical connections to and comparisons against another multifractal formalism, referred to as multifractal detrended fluctuation analysis, are achieved. The performance of the proposed p-leader multifractal formalism is studied and compared to previous formalisms using synthetic multifractal signals and images, illustrating its theoretical and practical benefits. The present contribution is complemented by a companion article studying in depth the theoretical properties of p-exponents and the rich classification of local singularities it permits.

Geophysical Research Letters, 2015

It has been shown that tropical climates can be notably influenced by the decadal solar cycle; ho... more It has been shown that tropical climates can be notably influenced by the decadal solar cycle; however, the relationship between this solar forcing and the tropical Amazon River has been overlooked in previous research. In this study, we reveal evidence of such a link by analyzing a 1903–2013 record of Amazon discharge. We identify a decadal flow cycle that is anticorrelated with the solar activity measured by the decadal sunspot cycle. This relationship persists through time and appears to result from a solar influence on the tropical Atlantic Ocean. The amplitude of the decadal solar signal in flow is apparently modulated by the interdecadal North Atlantic variability. Because Amazonia is an important element of the planetary water cycle, our findings have implications for studies on global change.

In many biological systems it is crucial to detect changes, as accurate as possible, in the param... more In many biological systems it is crucial to detect changes, as accurate as possible, in the parameters that govern their dynamics. In this work we propose a new method to perform an online automatic detection of such changes, making use of a well known nonlinear forecasting algorithm. The approach takes advantage of the characterization of an interval of a signal by the reconstruction of its phase space through time-delay embedding. To this end, the optimal delay and embedding dimension are estimated, and a method is proposed for determining the forecasting parameters, after which it is possible to predict future values of the studied signal. In this novel approach the method is used as a way of detecting changes in the dynamics of a system, given that the forecast is performed using a template of the signal where its parameters remain constant. At this point, the measure of the prediction error is used to detect a change in the dynamics of the system. We also propose a second estimator of this change, namely prediction failure, which is a stronger binary estimator of change in the dynamics. The results are analyzed by a cumulative sum algorithm (CUSUM) to obtain a detection point. In order to test their behavior, both methods are applied to deterministic discrete and continuos synthesized data, and to a simulated biological model.

Physica A: Statistical Mechanics and its Applications, 2016

Multifractal analysis aims to characterize signals, functions, images or fields, via the fluctuat... more Multifractal analysis aims to characterize signals, functions, images or fields, via the fluctuations of their local regularity along time or space, hence capturing crucial features of their temporal/spatial dynamics. Multifractal analysis is becoming a standard tool in signal and image processing, and is nowadays widely used in numerous applications of different natures. Its common formulation relies on the measure of local regularity via the Hölder exponent, by nature restricted to positive values, and thus to locally bounded functions or signals. It is here proposed to base the quantification of local regularity on p-exponents, a novel local regularity measure potentially taking negative values. First, the theoretical properties of p-exponents are studied in detail. Second, wavelet-based multiscale quantities, the p-leaders, are constructed and shown to permit accurate practical estimation of p-exponents. Exploiting the potential dependence with p, it is also shown how the collection of p-exponents enriches the classification of locally singular behaviors in functions, signals or images. The present contribution is complemented by a companion article developing the p-leader based multifractal formalism associated to p-exponents.

La segmentaci´on autom´atica del habla es importante en distintas aplicaciones. Los m´etodos util... more La segmentaci´on autom´atica del habla es importante en distintas aplicaciones. Los m´etodos utilizados comunmente se basan en modelos ocultos de Markov. Estos modelan estad´ısticamente las unidades fon´eticas y realizan una alineaci´on forzada de los datos seg´un una transcripci´on conocida. Este proceso es costoso y consume tiempo debido a la gran cantidad de datos necesarios para entrenar el sistema. Como soluci´on se han propuesto procedimientos de segmentaci´on independientes del texto. Estos detectan transiciones en la evoluci´on de los par´ametros que representan la se˜nal de habla. En estos procedimientos la forma de representar o parametrizar la se˜nal juega un rol central. En este trabajo se proponen nuevas parametrizaciones basadas en la entrop´ıa multiresoluci´on continua, utilizando entrop´ıa Shannon, y en la divergencia multiresoluci´on continua, mediante la distancia Kullback-Leibler. Dichas propuestas se comparan con la parametrizaci´on Melbank cl´asica. Los resultados muestran que el desempe˜no del algoritmo de segmentaci´on se incrementa con estas alternativas. En particular, la parametrizaci´on basada en la divergencia multiresoluci´on continua muestra los mejores resultados, incrementando el n´umero de l´ımites correctamente detectados y disminuyendo la cantidad de puntos insertados erroneamente. Esto sugiere que estas parametrizaciones proveen una mejor informaci´on, relacionada con caracter´ısticas ac´usticas del habla vinculadas a las transiciones fon´emicas.

Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439)

Abstract The application of complexity measures to the analysis of different biological signals h... more Abstract The application of complexity measures to the analysis of different biological signals have contributed to give a better understanding of the dynamical systems involved in their generation. In this work we present a comparative study of the complexity of speech signals from subjects with normal phonation and patients with laryngeal pathologies of the vocal system. Different complexity measures were considered in this study. Our results suggest that some of them would allow to discriminate between normal and pathological ...

While tested with noisy signals, it has been observed deterioration in the performance of automat... more While tested with noisy signals, it has been observed deterioration in the performance of automatic speech recognition systems trained with clean signals. In this paper we propose to introduce new parameters to a classical MFCC parametrization to overcome this situation. Continuous multiresolution entropy have shown to be robust to additive noise in applications to different physiological signals. In previous works temporal Shannon and Tsallis entropies, and their corresponding divergences, have been included in different speech related applications. Here we extend the continuous multiresolution entropy notion to different divergences. These parameters are introduced as new dimensions at the pre-processing stage of a speech recognizer and we compare the results obtained with the temporal measures. These new parametrizations are tested with speech signals corrupted with babble and white noise. Their performance are compared with the classical mel cepstra parametrization. Results suggest that information measures, specially those related to multiresolution divergences, provide valuable information that could be considered as an extra component in a pre-processing stage.

December 13-15, 2007: Firenze, Italy, ed. by C. Manfredi, ISBN 978 88-8453-673-3 (print) ISBN 978... more December 13-15, 2007: Firenze, Italy, ed. by C. Manfredi, ISBN 978 88-8453-673-3 (print) ISBN 978-88-8453-674-7 (online) © Firenze university press, 2007. Abstract: Techniques for the visualization of highdimensional data are common in exploratory data analysis and can be very useful for gaining an intuition into the structure of a data set. The classical method of principal component analysis is the one most often employed, however in recent years a number of other nonlinear techniques have been introduced. In the present paper, principal component analysis, and two newer methods, are applied to a set of speech data and their results are compared.

The present study deals with the problem of extracting information contained in complex signals. ... more The present study deals with the problem of extracting information contained in complex signals. We assume that time dependent non-linear systems are the source of these signals. Two problems of choice are to be confronted, namely, i) how to represent the signal, ie, in mathematical parlance, to select an appropriate basis, and ii) which information measure to employ. Both problems are discussed in this communication. With respect to the first one, we compare the classical Fourier analysis with the more modern wavelet based ...

Digital Signal Processing, 2015

Empirical mode decomposition (EMD) is an adaptive (data-driven) method to decompose non-linear an... more Empirical mode decomposition (EMD) is an adaptive (data-driven) method to decompose non-linear and non-stationary signals into AM-FM components. Despite its well-known usefulness, one of the major EMD drawbacks is its lack of mathematical foundation, being defined as an algorithm output. In this paper we present an alternative formulation for the EMD method, based on unconstrained optimization. Unlike previous optimization-based efforts, our approach is simple, with an analytic solution, and its algorithm can be easily implemented. By making no explicit use of envelopes to find the local mean, possible inherent problems of the original EMD formulation (such as the under-and overshoot) are avoided. Classical EMD experiments with artificial signals overlapped in both time and frequency are revisited, and comparisons with other optimization-based approaches to EMD are made, showing advantages for our proposal both in recovering known components and computational times. A voice signal is decomposed by our method evidencing some advantages in comparison with traditional EMD and noise-assisted versions. The new method here introduced catches most flavors of the original EMD but with a more solid mathematical framework, which could lead to explore analytical properties of this technique.

2009 17th European Signal Processing Conference, 2009

In this work, a new instantaneous fundamental frequency extraction method is presented, with the ... more In this work, a new instantaneous fundamental frequency extraction method is presented, with the attention especially focused on its robustness for pathological voices processing. It is based on the Ensemble Empirical Mode Decomposition (EEMD) algorithm, which is a completely data-driven method for signal decomposition into a sum of AM - FM components, called Intrinsic Mode Functions (IMFs) or modes. Our results show that the speech fundamental frequency can be captured in a single IMF. We also propose an algorithm for selecting the mode where the fundamental frequency can be found, based on the logarithm of the power of the IMFs. The instantaneous frequency is then extracted by means of well-known techniques. The behaviour of the proposed method is compared with other two ones (Robust Algorithm for Pitch Tracking -RAPT- and auto-correlation based algorithms), both in normal and pathological sustained vowels.

Las senales biomedicas, como el electrocardiograma, el electroencefalograma, o la senal de voz, t... more Las senales biomedicas, como el electrocardiograma, el electroencefalograma, o la senal de voz, tienen en comun caracteristicas de no estacionariedad y no linealidad. Aunque enmuchas aplicaciones se considera que se trata de senales estacionarias procedentes de sistemas lineales, esta simplificacion constituye una hipotesis de trabajo valida solo como una aproximacion que permite la aplicacion de tecnicas clasicas deanalisis de senales. Muchos trastornos que afectan a uno o varios organos pueden ser detectados a traves de un correcto analisis de las senales en cuya produccion estan involucrados. Sin embargo, debe atenderse al hecho de que una senal procedente de un sistema patologico se aleja aun mas de las condiciones hipoteticas de estacionariedad y linealidad. Se desprende de esta circunstancia la necesidad de abordar el analisis de las senales biomedicas mediante tecnicas no convencionales que permitan su tratamiento en un marco que tenga en cuenta sus caracteristicas de no esta...

VI Latin American Congress on Biomedical Engineering CLAIB 2014, Paraná, Argentina 29, 30 & 31 October 2014, 2015

Nonlinear Signal and Image Processing, 1999

Speech Communication, 2015

This paper deals with a particular aspect of the relation between signal analysis and nonlinear d... more This paper deals with a particular aspect of the relation between signal analysis and nonlinear dynamics, which is the detection of changes in parameters of nonlinear dynamical systems from the information obtained using experimental data. We propose a combination of the multiresolution wavelet analysis with the idea of entropy. We show that it provides a new tool, that we calle multiresolution entropy (MRE), useful for analyzing nonstationary signals and for the detection and localization of slight changes in nonlinear ...

The present work evaluates the intelligibility and the quality of speech signals after being proc... more The present work evaluates the intelligibility and the quality of speech signals after being processed using a group of noise reduction techniques. The intelligibility is measured in percentage of correctly recognized words in a subjective test, and the most frequent phonetic sustitutions are discussed in terms of confusion matrices. The quality of the obtained signals is evaluated also in an objective way starting from a group of measures selected ad-hoc. In this work the evaluation is presented for some of the most commonly employed classic techniques, such as Spectral Subtraction, Wiener and Ephraim-Malah filtering. The results of the preliminary evaluation of more recent techniques are also shown, as the ones based in the Wavelet transform. The relative performance of each considered algorithm is presented and discussed.

In this paper an algorithm based on the ensemble empirical mode decomposition (EEMD) is presented... more In this paper an algorithm based on the ensemble empirical mode decomposition (EEMD) is presented. The key idea on the EEMD relies on averaging the modes obtained by EMD applied to several realizations of Gaussian white noise added to the original signal. The resulting decomposition solves the EMD mode mixing problem, however it introduces new ones. In the method here proposed, a particular noise is added at each stage of the decomposition and a unique residue is computed to obtain each mode. The resulting decomposition is complete, with a numerically negligible error. Two examples are presented: a discrete Dirac delta function and an electrocardiogram signal. The results show that, compared with EEMD, the new method here presented also provides a better spectral separation of the modes and a lesser number of sifting iterations is needed, reducing the computational cost.

Physica A: Statistical Mechanics and its Applications, 2016

Multifractal analysis studies signals, functions, images or fields via the fluctuations of their ... more Multifractal analysis studies signals, functions, images or fields via the fluctuations of their local regularity along time or space, which capture crucial features of their temporal/spatial dynamics. It has become a standard signal and image processing tool and is commonly used in numerous applications of different natures. In its common formulation, it relies on the Hölder exponent as a measure of local regularity, which is by nature restricted to positive values and can hence be used for locally bounded functions only. In this contribution, it is proposed to replace the Hölder exponent with a collection of novel exponents for measuring local regularity, the p-exponents. One of the major virtues of p-exponents is that they can potentially take negative values. The corresponding wavelet-based multiscale quantities, the p-leaders, are constructed and shown to permit the definition of a new multifractal formalism, yielding an accurate practical estimation of the multifractal properties of real-world data. Moreover, theoretical and practical connections to and comparisons against another multifractal formalism, referred to as multifractal detrended fluctuation analysis, are achieved. The performance of the proposed p-leader multifractal formalism is studied and compared to previous formalisms using synthetic multifractal signals and images, illustrating its theoretical and practical benefits. The present contribution is complemented by a companion article studying in depth the theoretical properties of p-exponents and the rich classification of local singularities it permits.

Geophysical Research Letters, 2015

It has been shown that tropical climates can be notably influenced by the decadal solar cycle; ho... more It has been shown that tropical climates can be notably influenced by the decadal solar cycle; however, the relationship between this solar forcing and the tropical Amazon River has been overlooked in previous research. In this study, we reveal evidence of such a link by analyzing a 1903–2013 record of Amazon discharge. We identify a decadal flow cycle that is anticorrelated with the solar activity measured by the decadal sunspot cycle. This relationship persists through time and appears to result from a solar influence on the tropical Atlantic Ocean. The amplitude of the decadal solar signal in flow is apparently modulated by the interdecadal North Atlantic variability. Because Amazonia is an important element of the planetary water cycle, our findings have implications for studies on global change.

In many biological systems it is crucial to detect changes, as accurate as possible, in the param... more In many biological systems it is crucial to detect changes, as accurate as possible, in the parameters that govern their dynamics. In this work we propose a new method to perform an online automatic detection of such changes, making use of a well known nonlinear forecasting algorithm. The approach takes advantage of the characterization of an interval of a signal by the reconstruction of its phase space through time-delay embedding. To this end, the optimal delay and embedding dimension are estimated, and a method is proposed for determining the forecasting parameters, after which it is possible to predict future values of the studied signal. In this novel approach the method is used as a way of detecting changes in the dynamics of a system, given that the forecast is performed using a template of the signal where its parameters remain constant. At this point, the measure of the prediction error is used to detect a change in the dynamics of the system. We also propose a second estimator of this change, namely prediction failure, which is a stronger binary estimator of change in the dynamics. The results are analyzed by a cumulative sum algorithm (CUSUM) to obtain a detection point. In order to test their behavior, both methods are applied to deterministic discrete and continuos synthesized data, and to a simulated biological model.

Physica A: Statistical Mechanics and its Applications, 2016

Multifractal analysis aims to characterize signals, functions, images or fields, via the fluctuat... more Multifractal analysis aims to characterize signals, functions, images or fields, via the fluctuations of their local regularity along time or space, hence capturing crucial features of their temporal/spatial dynamics. Multifractal analysis is becoming a standard tool in signal and image processing, and is nowadays widely used in numerous applications of different natures. Its common formulation relies on the measure of local regularity via the Hölder exponent, by nature restricted to positive values, and thus to locally bounded functions or signals. It is here proposed to base the quantification of local regularity on p-exponents, a novel local regularity measure potentially taking negative values. First, the theoretical properties of p-exponents are studied in detail. Second, wavelet-based multiscale quantities, the p-leaders, are constructed and shown to permit accurate practical estimation of p-exponents. Exploiting the potential dependence with p, it is also shown how the collection of p-exponents enriches the classification of locally singular behaviors in functions, signals or images. The present contribution is complemented by a companion article developing the p-leader based multifractal formalism associated to p-exponents.

La segmentaci´on autom´atica del habla es importante en distintas aplicaciones. Los m´etodos util... more La segmentaci´on autom´atica del habla es importante en distintas aplicaciones. Los m´etodos utilizados comunmente se basan en modelos ocultos de Markov. Estos modelan estad´ısticamente las unidades fon´eticas y realizan una alineaci´on forzada de los datos seg´un una transcripci´on conocida. Este proceso es costoso y consume tiempo debido a la gran cantidad de datos necesarios para entrenar el sistema. Como soluci´on se han propuesto procedimientos de segmentaci´on independientes del texto. Estos detectan transiciones en la evoluci´on de los par´ametros que representan la se˜nal de habla. En estos procedimientos la forma de representar o parametrizar la se˜nal juega un rol central. En este trabajo se proponen nuevas parametrizaciones basadas en la entrop´ıa multiresoluci´on continua, utilizando entrop´ıa Shannon, y en la divergencia multiresoluci´on continua, mediante la distancia Kullback-Leibler. Dichas propuestas se comparan con la parametrizaci´on Melbank cl´asica. Los resultados muestran que el desempe˜no del algoritmo de segmentaci´on se incrementa con estas alternativas. En particular, la parametrizaci´on basada en la divergencia multiresoluci´on continua muestra los mejores resultados, incrementando el n´umero de l´ımites correctamente detectados y disminuyendo la cantidad de puntos insertados erroneamente. Esto sugiere que estas parametrizaciones proveen una mejor informaci´on, relacionada con caracter´ısticas ac´usticas del habla vinculadas a las transiciones fon´emicas.

Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439)

Abstract The application of complexity measures to the analysis of different biological signals h... more Abstract The application of complexity measures to the analysis of different biological signals have contributed to give a better understanding of the dynamical systems involved in their generation. In this work we present a comparative study of the complexity of speech signals from subjects with normal phonation and patients with laryngeal pathologies of the vocal system. Different complexity measures were considered in this study. Our results suggest that some of them would allow to discriminate between normal and pathological ...

While tested with noisy signals, it has been observed deterioration in the performance of automat... more While tested with noisy signals, it has been observed deterioration in the performance of automatic speech recognition systems trained with clean signals. In this paper we propose to introduce new parameters to a classical MFCC parametrization to overcome this situation. Continuous multiresolution entropy have shown to be robust to additive noise in applications to different physiological signals. In previous works temporal Shannon and Tsallis entropies, and their corresponding divergences, have been included in different speech related applications. Here we extend the continuous multiresolution entropy notion to different divergences. These parameters are introduced as new dimensions at the pre-processing stage of a speech recognizer and we compare the results obtained with the temporal measures. These new parametrizations are tested with speech signals corrupted with babble and white noise. Their performance are compared with the classical mel cepstra parametrization. Results suggest that information measures, specially those related to multiresolution divergences, provide valuable information that could be considered as an extra component in a pre-processing stage.

December 13-15, 2007: Firenze, Italy, ed. by C. Manfredi, ISBN 978 88-8453-673-3 (print) ISBN 978... more December 13-15, 2007: Firenze, Italy, ed. by C. Manfredi, ISBN 978 88-8453-673-3 (print) ISBN 978-88-8453-674-7 (online) © Firenze university press, 2007. Abstract: Techniques for the visualization of highdimensional data are common in exploratory data analysis and can be very useful for gaining an intuition into the structure of a data set. The classical method of principal component analysis is the one most often employed, however in recent years a number of other nonlinear techniques have been introduced. In the present paper, principal component analysis, and two newer methods, are applied to a set of speech data and their results are compared.

The present study deals with the problem of extracting information contained in complex signals. ... more The present study deals with the problem of extracting information contained in complex signals. We assume that time dependent non-linear systems are the source of these signals. Two problems of choice are to be confronted, namely, i) how to represent the signal, ie, in mathematical parlance, to select an appropriate basis, and ii) which information measure to employ. Both problems are discussed in this communication. With respect to the first one, we compare the classical Fourier analysis with the more modern wavelet based ...

Digital Signal Processing, 2015

Empirical mode decomposition (EMD) is an adaptive (data-driven) method to decompose non-linear an... more Empirical mode decomposition (EMD) is an adaptive (data-driven) method to decompose non-linear and non-stationary signals into AM-FM components. Despite its well-known usefulness, one of the major EMD drawbacks is its lack of mathematical foundation, being defined as an algorithm output. In this paper we present an alternative formulation for the EMD method, based on unconstrained optimization. Unlike previous optimization-based efforts, our approach is simple, with an analytic solution, and its algorithm can be easily implemented. By making no explicit use of envelopes to find the local mean, possible inherent problems of the original EMD formulation (such as the under-and overshoot) are avoided. Classical EMD experiments with artificial signals overlapped in both time and frequency are revisited, and comparisons with other optimization-based approaches to EMD are made, showing advantages for our proposal both in recovering known components and computational times. A voice signal is decomposed by our method evidencing some advantages in comparison with traditional EMD and noise-assisted versions. The new method here introduced catches most flavors of the original EMD but with a more solid mathematical framework, which could lead to explore analytical properties of this technique.

2009 17th European Signal Processing Conference, 2009

In this work, a new instantaneous fundamental frequency extraction method is presented, with the ... more In this work, a new instantaneous fundamental frequency extraction method is presented, with the attention especially focused on its robustness for pathological voices processing. It is based on the Ensemble Empirical Mode Decomposition (EEMD) algorithm, which is a completely data-driven method for signal decomposition into a sum of AM - FM components, called Intrinsic Mode Functions (IMFs) or modes. Our results show that the speech fundamental frequency can be captured in a single IMF. We also propose an algorithm for selecting the mode where the fundamental frequency can be found, based on the logarithm of the power of the IMFs. The instantaneous frequency is then extracted by means of well-known techniques. The behaviour of the proposed method is compared with other two ones (Robust Algorithm for Pitch Tracking -RAPT- and auto-correlation based algorithms), both in normal and pathological sustained vowels.

Las senales biomedicas, como el electrocardiograma, el electroencefalograma, o la senal de voz, t... more Las senales biomedicas, como el electrocardiograma, el electroencefalograma, o la senal de voz, tienen en comun caracteristicas de no estacionariedad y no linealidad. Aunque enmuchas aplicaciones se considera que se trata de senales estacionarias procedentes de sistemas lineales, esta simplificacion constituye una hipotesis de trabajo valida solo como una aproximacion que permite la aplicacion de tecnicas clasicas deanalisis de senales. Muchos trastornos que afectan a uno o varios organos pueden ser detectados a traves de un correcto analisis de las senales en cuya produccion estan involucrados. Sin embargo, debe atenderse al hecho de que una senal procedente de un sistema patologico se aleja aun mas de las condiciones hipoteticas de estacionariedad y linealidad. Se desprende de esta circunstancia la necesidad de abordar el analisis de las senales biomedicas mediante tecnicas no convencionales que permitan su tratamiento en un marco que tenga en cuenta sus caracteristicas de no esta...

VI Latin American Congress on Biomedical Engineering CLAIB 2014, Paraná, Argentina 29, 30 & 31 October 2014, 2015

Nonlinear Signal and Image Processing, 1999

Speech Communication, 2015