1e0a: A Computational Approach to Rhythm Training

We present a computational assessment system that promotes the learning of basic rhythmic patterns. The system generates multiple rhythmic patterns with increasing complexity within various cycle lengths. For a generated rhythm pattern the performance assessment of the learner is characterized with summary statistics estimated from the onsets of the learner's performance and provided as feedback. When performance assessment feedback is within certain error bounds, the system proceeds to generate a new pattern of increased complexity. The system thus mimics a learner-teacher relationship as the learner progresses in their feedback-based learning. The choice of progression within a cycle for each pattern is determined by a predefined complexity metric. This metric is based on a coded element model for the perceptual processing of sequential stimuli. A model earlier proposed for a sequence of tones and non-tones, is now extended for onsets and silences. This system is developed into a web-based application and provides accessibility for learning purposes. Analysis of learner performance assessments from a pilot user study shows us that the complexity metric is moderately indicative of the perceptual processing of rhythm patterns and with further enhancements can be used for rhythm learning and future syllabus generation of rhythm training tools.