Melodic patterns and tonal cadences: Bayesian learning of cadential categories from contrapuntal information

ABSTRACT

Recent work has shown that authentic and half cadences can be identified via harmonic features in both supervised and unsupervised settings, suggesting that humans may use such cues in perceiving and learning cadences. The present study tests melodic features in these same tasks. Both n-gram models and profile hidden Markov models of melodic patterns are used for supervised classification and unsupervised learning of cadences in Classical string quartets. Success is achieved at the supervised task but not the unsupervised task, indicating that melodic cues would help in perceiving cadences but not in learning to perceive them.

KEYWORDS:

• Cadence
• melodic pattern analysis
• n-gram model
• profile hidden Markov model
• Bayesian learning

Disclosure statement

No potential conflict of interest was reported by the author.