Double Fourier analysis for Emotion Identification in Voiced Speech

We propose a novel analysis alternative, based on two Fourier Transforms for emotion recognition from speech -- Fourier analysis allows for display and synthesizes different signals, in terms of power spectral density distributions -- A spectrogram of the voice signal is obtained performing a short time Fourier Transform with Gaussian windows, this spectrogram portraits frequency related features, such as vocal tract resonances and quasi-periodic excitations during voiced sounds -- Emotions induce such characteristics in speech, which become apparent in spectrogram time-frequency distributions -- Later, the signal time-frequency representation from spectrogram is considered an image, and processed through a 2-dimensional Fourier Transform in order to perform the spatial Fourier analysis from it -- Finally features related with emotions in voiced speech are extracted and presented

Sensitivity analysis in optimized parametric curve fitting

Purpose – Curve fitting from unordered noisy point samples is needed for surface reconstruction in many applications -- In the literature, several approaches have been proposed to solve this problem -- However, previous works lack formal characterization of the curve fitting problem and assessment on the effect of several parameters (i.e. scalars that remain constant in the optimization problem), such as control points number (m), curve degree (b), knot vector composition (U), norm degree (k), and point sample size (r) on the optimized curve reconstruction measured by a penalty function (f) -- The paper aims to discuss these issues -- Design/methodology/approach - A numerical sensitivity analysis of the effect of m, b, k and r on f and a characterization of the fitting procedure from the mathematical viewpoint are performed -- Also, the spectral (frequency) analysis of the derivative of the angle of the fitted curve with respect to u as a means to detect spurious curls and peaks is explored -- Findings - It is more effective to find optimum values for m than k or b in order to obtain good results because the topological faithfulness of the resulting curve strongly depends on m -- Furthermore, when an exaggerate number of control points is used the resulting curve presents spurious curls and peaks -- The authors were able to detect the presence of such spurious features with spectral analysis -- Also, the authors found that the method for curve fitting is robust to significant decimation of the point sample -- Research limitations/implications - The authors have addressed important voids of previous works in this field -- The authors determined, among the curve fitting parameters m, b and k, which of them influenced the most the results and how -- Also, the authors performed a characterization of the curve fitting problem from the optimization perspective -- And finally, the authors devised a method to detect spurious features in the fitting curve -- Practical implications – This paper provides a methodology to select the important tuning parameters in a formal manner -- Originality/value - Up to the best of the knowledge, no previous work has been conducted in the formal mathematical evaluation of the sensitivity of the goodness of the curve fit with respect to different possible tuning parameters (curve degree, number of control points, norm degree, etc.)

Modelación de la prima de riesgo que asumen los agentes en transacciones a plazo a través de los factores que influyen y explican el comportamiento de la tasa de cambio de la moneda colombiana con respecto a la de Estados Unidos

En este trabajo se pretende establecer que factores fundamentales influyen en el movimiento de la tasa de cambio COP/USD en un periodo intra-diario de forma horaria, para así poder establecer un modelo que ayude a estimar la prima de riesgo de la tasa de cambio colombiana -- Basados en Pantoja (2012)1, se pretende la aplicación de un modelo VAR (vectores autorregresivos) para estimar la prima de riesgo de la tasa de cambio, donde se encontró que este modelo no es el modelo más adecuado para explicar la serie de datos utilizada, por lo que se propone un modelo GARCH para modelar la serie -- Se encontró que hay factores fundamentales que explican la prima, como lo son el WTI, el S&P500 y la tasa de cambio EUR/USD

FOR-TSM: desarrollo de una herramienta de pronósticos con modelos de series de tiempo

Uno de los temas más complejos y necesarios en los cursos de Administración de Operaciones, es el uso de los pronósticos con modelos de series de tiempo (TSM por sus siglas en inglés) -- Para facilitar el entendimiento y ayudar a los estudiantes a comprender fácilmente los pronósticos de demanda, este proyecto presenta FOR TSM, una herramienta desarrollada en MS Excel VBA® -- La herramienta fue diseñada con una Interfaz gráfica de Usuario (GUI por sus siglas en inglés) para explicar conceptos fundamentales como la selección de los parámetros, los valores de inicialización, cálculo y análisis de medidas de desempeño y finalmente la selección de modelos