Harmonic clusters and tonal cadences: Bayesian learning without chord identification

Authentic and half cadences not only serve as structural cornerstones of tonal music, but are readily perceived by both expert and novice listeners. Yet it is unclear how untrained listeners, who have never studied harmonic theory, come to recognise cadential patterns that are most often codified as short series of chords, such as V-7-I or ii(6)-V. This study addresses both questions by analysing a corpus of string-quartet excerpts whose authentic and half cadences were identified by two musical experts. A cognitively plausible model of harmonic learning, which is based on clusters of scale-degree distributions abstracted from the data, is proposed. After assessing the correlation of this model's output with cadential categories, Bayesian or near-Bayesian frameworks are used to learn these categories from the model in both supervised and unsupervised contexts. The model succeeds in not only spotting cadences but also identifying cadential categories from unlabelled data. The model's relationship to relevant perceptual research, as well as the results' implications for human learning and detection of cadences, is discussed.