PROBABILITY AS METADATA: EVENT DETECTION IN MUSIC USING ICA AS A CONDITIONAL DENSITY MODEL
Samer Abdallah, Mark D. Plumbley · 2003
We consider the problem of detecting note onsets in music under the hypothesis that the onsets, and events in general, are essentially surprising moments, and that event detection should therefore be based on an explicit probability model of the sensory input, which generates a moment-by-moment trace of the probability of each observation as it is made. Relatively unexpected events should thus appear as clear spikes. In this way, several well known methods of onset detection can be understood in terms of an implicit probability model. We apply ICA to the problem as an adaptive non-Gaussian model, and investigate the use of ICA as a conditional probability model. The results obtained using several methods on two extracts of piano music are presented and compared. Finally, we tentatively suggest an information theoretic interpretation of the approach.