A Computational Method for Empirically Validating Synchronisation between Musical Phrase Arcs and Autonomic Variables

Prior research suggests listeners' autonomic variables may entrain with musical phrase arcs.Here, we apply a computational technique for automatic extraction of probabilistic phrase arc boundaries from recorded music audio and compare them to envelopes derived from physiological signals (respiration and RR intervals).The objective is to automate the evaluation of synchronisation of autonomic reactions to musical phrases, thereby assessing music's ability to regulate physiological states.20 participants' (10 women, aged 3911 years old) physiological signals (respiration and RR intervals) were recorded whilst listening to Prokofiev playing Prokofiev's Gavotte Op.12 No.2 rendered on a reproducing piano.Phrase arcs are computed from the loudness profile using a novel Bayesian approach incorporating dynamic programming.Results show increased curve similarity between the loudness phrase arcs and the physiological signal envelopes for the original data compared to the surrogate, with p-values of 0.057 and 0.021 based on the binomial test.We have presented a fully automated data-driven technique for testing hypotheses about entrainment between musical phrase arcs and autonomic variables.Preliminary findings support the potential of employing music with specific structural qualities to modulate physiological signals.