A Lempel-Ziv like approach for signal classification

Abstract

In this paper, the seminal method proposed by Abraham Lempel and Jacob Ziv, aimed at the complexity analysis of sequences of symbols, was modified to compare similarities between two sequences. This modification allowed the creation of a new criterion which can replace likelihood in some pattern recognition applications. Moreover, to allow for analysis and comparison of multivariate continuously valued patterns, we also present a simple adaptation of the Lempel-Ziv's method to time-sampled signals. To illustrate the usefulness of these proposed tools, two sets of experimental results are presented, namely: one on speaker identity verification (biometrics) and another on healthcare signal detection. Both experiments yield promising performances. Moreover, as compared to a conventional pattern recognition method, the new approach provided better performances in terms of Equal Error Ratio in speaker verification experiments.