A method for computing the similarity of metrical rhythmic patterns is described as applied to the audio signal of recorded music. For each rhythm, a combined feature vector of metrical profile and syncopation, separated by spectral subbands, hypermetrical profile, and tempo are compared. The descriptive capability of this feature vector is evaluated by it's use in a machine learning rhythm classification task, identifying ballroom dance styles using a support vector machine algorithm. Results indicate that with the full feature vector a result of 67% is achieved. This improves on previous results using rhythmic patterns alone, but does not exceed the best reported results. By evaluating individual features, measures of metrical, syncopation and hypermetrical profile are found to play a greater role than tempo in aiding discrimination.

A computational multi-resolution model of musical rhythm expectation has been recently proposed based on cumulative evidence of rhythmic time-frequency ridges (Smith & Honing 2008a). This model was shown to demonstrate the emergence of musical meter from a bottom-up data processing model, thus clarifying the role of top-down expectation. Such a multiresolution time-frequency model of rhythm has also been previously demonstrated to track musical rubato well, with both synthesised (Smith & Honing 2008b) and performed audio examples (Coath et. al 2009). The model is evaluated for it's capability to generate accurate expectation from human musical performances. The musical performances consist of 63 monophonic rhythms from MIDI keyboard performances, and 50 audio recordings of popular music. The model generates expectations as forward predictions of times of future notes, a confidence weighting of the expectation, and a precision region. Evaluation consisted of generating successive expectations from an expanding fragment of the rhythm. In the case of the monophonic MIDI rhythms, these expectations were then scored by comparison against the onset times of notes actually then performed. The evaluation is repeated across each rhythm. In the case of the audio recording data, where beat annotations exist, but individual note onsets are not annotated, forward expectation is measured against the beat period. Scores were computed using information retrieval measures of precision, recall and F-score (van Rijsbergen 1979) for each performance. Preliminary results show mean PRF scores of (0.297, 0.370, 0.326) for the MIDI performances, indicating performance well above chance (0.177, 0.219, 0.195), but well below perfection. A model of expectation of musical rhythm has been shown to be computable. This can be used as a measure of rhythmic complexity, by measuring the degree of contradiction to expectation. As such, a rhythmic complexity measure is then applicable in models of rhythmic similarity used in music information retrieval applications.

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.