Three-Parametric Cubic Interpolation for Estimating the Fundamental Frequency of the Speech Signal

Abstract

In this paper, we propose a three-parametric convolution kernel which is based on the one-parameter Keys kernel. The first part of the paper describes the structure of the three-parameter convolution kernel. Then, a certain analytical expression for finding the position of the maximum of the reconstructed function is given. The second part presents an algorithm for estimating the fundamental frequency of the speech signal processing in the frequency domain using Picking Picks methods and parametric cubic convolution. Furthermore, the results of experiments give the estimated fundamental frequency of speech and sinusoidal signals in order to select the optimal values of the parameters of the proposed convolution kernel. The results of the fundamental frequency estimation according to the mean square error are given by tables and graphics. Consequently, it is used as a basis for a comparative analysis. The analysis derived the optimal parameters of the kernel and the window function that generates the least MSE. Results showed a higher efficiency in comparison to two or three-parameter convolution kernel.