UC Davis

Musical stimuli present listeners with complex temporal information and rich periodic structure. Periodic patterns in music typically involve multiple hierarchical levels: a basic-level repeating pulse known as the "beat," and a higher-order grouping of beats into the "meter." Previous work has found that a musical stimulus's meter is predicted by recurring temporal patterns of note event onsets, measured by profiles of autocorrelation over time lags. Traditionally, that work has emphasized periodic structure in the timing of event onsets (i.e., repeating rhythms). Here, we suggest that musical meter is in fact a more general perceptual phenomenon, instantiating complex profiles of temporal dependencies across both event onsets and multiple feature dimensions in the actual content of events. We use classification techniques to test whether profiles of temporal dependencies in event onsets and in multiple types of event content predict musical meter. Applying random forest models to three musical corpora, we reproduce findings that profiles of temporal dependencies in note event onsets contain information about meter, but we find that profiles of temporal dependencies in pitch height, interval size, and tonal expectancy also contain such information, with high redundancy among temporal dependencies in event onsets and event content as predictors of meter. Moreover, information about meter is distributed across temporal dependencies at multiple time lags, as indicated by the baseline performance of an unsupervised classifier that selects the single time lag with maximum autocorrelation. Redundant profiles of temporal dependencies across multiple stimulus features may provide strong constraints on musical structure that inform listeners' predictive processes.