A multiresolution model of rhythmic expectancy

We describe a computational model of rhythmic cognition that predicts 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 components 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. Future expected times are computed using peaks in the accumulation of time-frequency ridges. This accumulation at the edge of the analysed time window forms a dynamic expectancy. We evaluate this model using data sets of expressively timed (or performed) and generated musical rhythms, by its ability to produce expectancy profiles which correspond to metrical profiles. The results show that rhythms of two different meters are able to be distinguished. Such a representation indicates that a bottom-up, data-oriented process (or a non-cognitive model) is able to reveal durations which match metrical structure from realistic musical examples. This then helps to clarify the role of schematic expectancy (top-down) and it's contribution to the formation of musical expectation.