Attenuated embedding estimators for speech signals

In an earlier paper we utilized techniques from the theory of nonlinear dynamical systems to define a notion of embedding threshold estimators. These estimators were based on the analysis of delay-coordinates embeddings of sets of coefficients of the measured signal in some chosen frame. One of the motivations behind the method was the desire of building an estimator that would perform equally well regardless of the type of white noise contamination. In this paper we explicitly write the structure of one particular algorithm, the attenuated embedding threshold estimator, and we show the performance of the algorithm when different types of white noise are added to speech time series.