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 non-Gaussian linear processes a Generalized Spectral Flatness Measure is developed, which estimates the excessive structure that is present in the signal due to the non-Gaussianity 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.