Trying different wavelets on the search for voice disorders sorting

This work describes the use of different DWTs - discrete wavelet transforms, like Haar, Daubechies, Coiflets, Symmlets and Spikelet in order to distinguish between normal and pathological human voices. All the transforms are used to evaluate some properties of the digitalized voice signals under analysis, as the power density spectrum and also a fractal dimension parameter is calculated. According to an ordinary threshold it is possible to estimate the voice as belonging to a normal or pathological patient, in respect to his or her larynx working. The results are compared and presented and the conclusions show it is possible to have a promising result based on a deterministic approach with low computational order of complexity, furthermore, it is possible to have a DSP real-time implementation.