Simultaneous Unsupervised Learning of Flamenco Metrical Structure, Hypermetrical Structure, and Multipart Structural Relations

We show how a new unsupervised approach to learning musical relationships can exploit Bayesian MAP induction of stochastic transduction grammars to overcome the challenges of learning complex relationships between multiple rhythmic parts that previously lay outside the scope of general computational approaches to music structure learning. A good illustrative genre is flamenco, which employs not only regular but also irregular hypermetrical structures that rapidly switch between 3/4 and 6/8 mediocompas blocks. Moreover, typical flamenco idioms employ heavy syncopation and sudden, misleading off-beat accents and patterns, while often elliding the downbeat accents that humans as well as existing meter-finding algorithms rely on, thus creating a high degree of listener “surprise” that makes not only the structural relations, but even the metrical structure itself, ellusive to learn. Flamenco musicians rely on both complex regular hypermetrical knowledge as well as irregular real-time clues to recognize when to switch meters and patterns. Our new approach envisions this as an integrated problem of learning a bilingual transduction, i.e., a structural relation between two languages—where there are different musical languages of, say, flamenco percussion versus zapateado footwork or palmas hand clapping. We apply minimum description length criteria to induce transduction grammars that simultaneously learn (1) the multiple metrical structures, (2) the hypermetrical structure that stochastically governs meter switching, and (3) the probabilistic transduction relationship between patterns of different rhythmic languages that enables musicians to predict when to switch meters and how to select patterns depending on what fellow musicians are generating.