5 resultados para GMM

em Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho"


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Discriminative training of Gaussian Mixture Models (GMMs) for speech or speaker recognition purposes is usually based on the gradient descent method, in which the iteration step-size, ε, uses to be defined experimentally. In this letter, we derive an equation to adaptively determine ε, by showing that the second-order Newton-Raphson iterative method to find roots of equations is equivalent to the gradient descent algorithm. © 2010 IEEE.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Pós-graduação em Agronomia (Horticultura) - FCA

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Pós-graduação em Zootecnia - FMVZ

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The objective of this work was to evaluate the effect of processing two corn hybrids conserved, dry and humid grains, the dry matter (DM) and crude protein (CP) degradability in situ. The particle size was determined and difference was verified in MGD (Medium Geometric Diameter) of processed ingredients. Three sheep were used with rumen canulated, in a completely randomized design, using a factorial outline 2 x 2 x 3, being two corn hybrid, two conservation methods and three processing forms (whole, coarsely and finely ground), with five times of incubation (3, 6, 12, 24 and 48 hours). The fraction A in SDC (silage of dent corn) of DM was superior to GDC (grain of dent corn) in all of the particles size. The ensiling process increased the DM solubility, reducing the fraction B in comparison to dry grain. The values regarding the fractions DP and DE the 5% per hour of the protein, were larger for SDC and GDC, it presents a decreasing when the incubation time advances. The fermentation rate was superior for SDC and GDC. The ensiling process has positive effect in the decreasing of DM and CP in comparison to GDC.