A Multiresolution Model of Rhythmic Expectancy

We describe a computational model of rhythmic cognition that pre-dicts expected onset times. A dynamic representation of musical rhythm, the multiresolution analysis using the continuous wavelet transform is used. This representation decomposes the temporal structure of a musical rhythm into time varying frequency compo-nents in the rhythmic frequency range (sample rate of 200Hz). Both expressive timing and temporal structure (score times) contribute in an integrated fashion to determine the temporal expectancies. Fu-ture expected times are computed using peaks in the accumulation of time-frequency ridges. This accumulation at the edge of the anal-ysed time window forms a dynamic expectancy. We evaluate this model using data sets of expressively timed (or performed) and gen-erated musical rhythms, by its ability to produce expectancy pro-files which correspond to metrical profiles. The results show tha