Examinando por Autor "Bastidas, Manuela"

Mostrando 1 - 5 de 5
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    Double Fourier analysis for Emotion Identification in Voiced Speech.
    (IOP PUBLISHING LTD, 2016-01-01) Bastidas, Manuela; Universidad EAFIT. Escuela de Ciencias; Modelado Matemático
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    Ítem
    A High-Order HDG Method with Dubiner Basis for Elliptic Flow Problems
    (Universidad EAFIT, 2020-11-11) Bastidas, Manuela; Lopez-Rodríguez, Bibiana; Osorio, Mauricio; Hasselt University; Universidad Nacional de Colombia
    We propose a standard hybridizable discontinuous Galerkin (HDG) method to solve a classic problem in fluids mechanics: Darcy’s law. This model describes the behavior of a fluid trough a porous medium and it is relevant to the flow patterns on the macro scale. Here we present the theoretical results of existence and uniqueness of the weak and discontinuous solution of the second order elliptic equation, as well as the predicted convergence order of the HDG method. We highlight the use and implementation of Dubiner polynomial basis functions that allow us to develop a general and efficient high order numerical approximation. We also show some numerical examples that validate the theoretical results.
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    Modeling perspective for the relevant market of voice services: Mobile to Mobile.
    (2015-10-19) Lucia Quintero M, O.; Jaramillo P, Fabian L.; Bastidas, Manuela; Universidad EAFIT. Escuela de Ciencias; Modelado Matemático
    The markets, associated to -mobile to mobile- services of voice, in Advanced Mobile Telecommunications services in Latin America have been subject to dominant operator regulatory processes, which are necessary to attain the market conditions...
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    A Novel emotion recognition technique from voiced-speech
    (IEEE, 2017-01-01) Uribe, Alejandro; Gomez, Alejandro; Bastidas, Manuela; Quintero, O. Lucia; Campo, Damian; Uribe, Alejandro; Gomez, Alejandro; Bastidas, Manuela; Quintero, O. Lucia; Campo, Damian; Universidad EAFIT. Departamento de Ciencias; Modelado Matemático
    In the framework of the beginning of the investigation due to a work of an undergraduate student, the authors at Mathematical Modeling Research Group (GRIMMAT) propose the use of emotion recognition algorithms previously developed by them adapting it to the FAU Aibo emotion corpus which was the database used in the INTERSPEECH 2009 Emotion Challenge. Firstly, by resampling the audio signal and windowing process, the audio signal is segmented. Next, each segment is decomposed through the discrete wavelet transform, then the descriptive characteristics of the decomposed signal are extracted. Finally, a supervised classification scheme is used. This paper presents the main results and conclusions obtained.