Singularity analysis of digital signals through the evaluation of their unpredictable point manifold

Abstract

The local singularity exponents of a signal are directly related to the distribution of information in it. This fact implies that accurate evaluation of such exponents opens the door to signal reconstruction and characterization of the dynamical parameters of the process originating the signal. Many practical implications arise in a context of digital signal processing, since the information on singularity exponents is usable for compact encoding, reconstruction and inference. Since singularity exponents are conceptually associated with differential calculus, its evaluation in a digital context is not straightforward and it requires the calculation of the unpredictable point manifold of the signal. In this paper, we present an algorithm for estimating the values of singularity exponents at every point of a digital signal of any dimension. We show that the key ingredient for robust and accurate reconstructibility performance lies on the definition of multiscale measures in the sense that they encode the degree of singularity and the local predictability at the same time.