Spectral Anticipations

The question of noise vs. structure is basic to many musical applications, as well as audio processing and sound understanding. In this paper we present a new measure for the extent of randomness present in a signal that we call Information Rate. This measure is formulated in terms of the reduction of uncertainly that an information processing system achieves when it forms anticipation about future values of a process based on its past. In the case of a simple source-filter signal model it is shown that this measure is equivalent to the well-known Spectral Flatness Measure that is commonly used in audio processing. The information rate is extended to multivariate processes such as a geometric signal, spectral or feature vector representations. The measure is tested on various synthetic, natural and musical signals. It is shown that it is capable of detecting structure in cases when standard methods based on signal spectrum seem to be unable to operate.