Gender classification based on voice signals using fuzzy models and optimization algorithms

Abstract

This paper describes a gender classification scheme based on voice signals in which 16 different fuzzy models are proposed and optimized using four bio-inspired optimization algorithms and the quasi-Newton method. The classification scheme considers four data sets and five different voice features to define the input values of an algorithm in the optimization process. The inputs of each fuzzy model define the mean and variance of their Gaussian membership functions, and their fitness is evaluated by the input values of the algorithm and mean squared error as objective function to be minimized. A comparative analysis between models, algorithms and data sets is made to obtain conclusions according to the results of each optimized model.

Optimization techniques on fuzzy inference systems to detect Xanthomonas campestris disease

“…The fuzzy systems, set with the training data (45-pixel values for sane o ill), were tested to verify a correct recognition of the pixels' value status through the MSE calculation between the predicted outcome and the obtained results. The latter shows a medium to low accuracy (as expected on a fuzzy inference system), as a consequence the membership functions definitions for input and output sets are modified using the genetic and Quasi-Newton algorithms stated above; for such modification, it is necessary to have the input from the fuzzy system and the expected outcome and the outcome obtained from both algorithms [28]. The aim is to get a global minimum to reduce the difference between the fuzzy inference system expected outcome and the current outcome through the adjustment of the membership functions' limits in the input and output of the fuzzy systems.…”