Non-Gaussian Source-Filter and Independent Components Generalizations of Spectral Flatness Measure

Spectral Flatness Measure is a well-known method for quantifying the amount of randomness (or “stochasticity”) that is present in a signal. This measure has been widely used in signal compression, audio characterization and retrieval. In this paper we present an information-theoretic generalization of this measure that is formulated in terms of a rate of growth of multi-information of a nonGaussian linear process. Two new measures are defined and methods for their estimation are presented: 1) considering a source-filter model, a Generalized Spectral Flatness Measure is developed that estimates the excessive structure due to non-Gaussianity of the innovation process, and 2) using a geometrical embedding, a block-wise information redundancy is formulated using signal representation in an Independent Components basis. The two measures are applied for the problem of voiced/unvoiced determination in speech signals and analysis of spectral (timbral) dynamics in musical signals.