A Dynamic Systems Approach to Modeling Human-Machine Rhythm Interaction.

Abstract

Rhythm is an inherent aspect of human behavior, present from infancy and embedded in cultural practices. At the core of rhythm perception lies meter anticipation, a spontaneous process in the human brain that typically occurs before actual beats. This anticipation can be framed as a time series prediction problem. From the perspective of human embodied system behavior, although many models have been developed for time series prediction, most prioritize accuracy over biological realism, contrasting with the natural imprecision of human internal clocks. Neuroscientific evidence, such as infants' natural meter synchronization, underscores the need for biologically plausible models. Therefore, we propose a neuron oscillator-based dynamic system that simulates human behavior during meter perception. The model introduces two tunable parameters for local and global adjustments, fine-tuning the oscillation combinations to emulate human-like rhythmic behavior. The experiments are conducted under three common scenarios encountered during human-machine interaction, demonstrating that the proposed model can exhibit human-like reactions. Additionally, experiments involving human-machine and interhuman interactions show that the model successfully replicates real-world rhythmic behavior, advancing toward more natural and synchronized human-machine rhythm interaction.