We present an information-theoretic measure for the amount of randomness or stochasticity that exists in a signal. This measure is formulated in terms of the rate of growth of multi-information for every new signal sample of the signal that is observed over time. In case of a Gaussian statistics it is shown that this measure is equivalent to the well-known spectral flatness measure that is commonly used in audio processing. For nonGaussian linear processes a generalized spectral flatness measure is developed, which estimates the excessive structure that is present in the signal due to the nonGaussianity of the innovation process. An estimator for this measure is developed using Negentropy approximation to the non-Gaussian signal and the innovation process statistics. Applications of this new measure are demonstrated for the problem of voiced/unvoiced determination, showing improved performance.
['Shlomo Dubnov']
Generalization of spectral flatness measure for non-Gaussian linear processes