Pseudo-periodic surrogate data method on voice signals

Abstract

In the following article, Pseudo-Periodic surrogate data method is described as a tool to detect the underlying dynamics existing in non-linear phenomena, in order to know beforehand the best approach when analyzing with these types of time series. This method is applied to voice signals, a non-linear phenomenon observed from the vocal tract, to try determine its underlying dynamic structure and therefore use the appropriate approach. Lempel-Ziv complexity, based on the counting of sequences, and Sample Entropy, based on the extent of the irregularity in a signal, are introduced as discriminating statistics for null hypothesis testing within the surrogate data method. In addition, a methodology is explained on how to apply this method to voice signals. Our results showed that Lempel-Ziv complexity rejects the proposed hypothesis while sample entropy gives results beyond expectation.