Rhythmic Similarity Using Metrical Profile Matching
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.