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. 1. SIMILARITY OF MUSICAL RHYTHMS A computational model of rhythmic similarity of music has applications in several fields of music research. In music information retrieval (MIR), measures of similarity matching listeners judgements promise retrieval that better matches users expectations. Identification of common rhythmic structures can be used in tracing musical sources in western and ethnological musicology. A concrete example application is “automatic DJing” [4] to create playlists that require a common mood, such as for dancing. Palmer and Krumhansl [13] argue that pre-established mental frameworks (“schemas”) for musical meter1 are used during listening. These schemas enable robust interpretation despite sometimes contradicting, ambiguous or absent objective cues. In this study they tested the types of mental structures for meter (“perceptual hierarchies”) evoked from simple event sequences. Listeners rated the relative acceptability of audible events at different locations in the metrical grid [13, Experiment 2]. They found a significant difference in performance between musicians and non-musicians, arguing that musicians hold more resilient representations of meter, which favours hierarchical subdivision of the measure, than the non-musicians. 1A periodic repetition of accentuation, notated in music as 4 , 3 4 etc. The complexity of a rhythm may contribute to how it is judged as similar to another rhythm [20, 9]. Several approaches to formalise rhythm complexity have been proposed [3, 17, 18], with experimental verification. Ladinig [9] had subjects rate sets of 2-4 musical rhythms according to complexity, chosen to compare different metrical positions. While an hierarchical, metrical structure was found in both musicians and non-musicians, serial primacy and recency effects were also found, particularly in non-musician subjects. While not the only factor, metrical structure can be seen to contribute to judgements of rhythmic similarity. This paper proposes a model of rhythmic similarity with the aim to match human judgements when comparing two pieces of recorded music. This model represents musical concepts such as meter, syncopation, hypermeter (grouping over multiple measures) and tempo. These form a combined feature vector of metrical and syncopation profile for separate spectral subbands, hypermetrical profile and median tempo, described in Section 3. The applicability of the features to generate similarity measures are evaluated in Section 4 by using them for a machine learning classification task, using the well developed support vector machine (SVM) learning function in Section 5.