🤖 AI Summary
This study addresses the representation, visualization, and quantification of regular structures in rhythmic data by proposing a “rhythmic motif” framework that models rhythms as fixed-length sequences of inter-onset intervals, decomposed into duration and pattern (the ratios between successive intervals). Building on this decomposition, the authors introduce pattern–duration plots and cluster transition networks to uncover rhythmic regularities. They reformulate the normalized Pairwise Variability Index (nPVI) as the mean deviation from isochrony and propose a more general measure of anisochrony alongside a novel concept termed “quantization.” This approach unifies existing visualization techniques and effectively reveals small-integer-ratio rhythmic structures in both synthetic and real-world datasets, establishing a more flexible and theoretically grounded framework for rhythm analysis.
📝 Abstract
This paper develops a framework for conceptualizing, visualizing, and measuring regularities in rhythmic data. I propose to think about rhythmic data in terms of interval segments: fixed-length groups of consecutive intervals, which can be decomposed into a duration and a pattern (the ratios between the intervals). This simple conceptual framework unifies three rhythmic visualization methods and yields a fourth: the pattern-duration plot. When paired with a cluster transition network, it intuitively reveals regularities in both synthetic and real-world rhythmic data. Moreover, the framework generalizes two common measures of rhythmic structure: rhythm ratios and the normalized pairwise variability index (nPVI). In particular, nPVI can be reconstructed as the average distance from isochrony, and I propose a more general measure of anisochrony to replace it. Finally, the novel concept of quantality may shed light on wider debates regarding small-integer-ratio rhythms.