Co-regulated timing in music ensembles: A Bayesian listener perspective

Abstract

Co-regulated timing in music ensembles Co-regulated timing in music ensembles: a Bayesian listener perspectiveCo-regulated timing in a music ensemble rests on the human capacity to coordinate actions in time. Here we explore the hypothesis that humans predict timing constancy in coordinated actions, in view of timing their own actions in line with the others. An algorithm (BListener) is presented that predicts timing constancy, using Bayesian inference about incoming timing data from the music ensemble. Smoothness and regularization parameters are explained and illustrated. The algorithm is then applied to a timing analysis of real data, first, to a choir consisting of four singers, then, to a dataset containing performances of duet singers. Global features of timing constancy, such as fluctuation and stability, correlate with human subjective estimates of the music ensembles' quality and associated experienced agency. The results suggest that computational modelling of co-regulated timing can lead to powerful insights and applications. In future work, BListener could serve as component in an artificial musician that plays along with human musicians in a music ensemble.

Enhancing human-human musical interaction through kinesthetic haptic feedback using wearable exoskeletons: theoretical foundations, validation scenarios, and limitations

“…For example, a stable musical rhythm and tempo require each musician in an ensemble to coordinate their actions in response to the rhythms played by other musicians. The co-regulation of the ensemble can be measured by onset extraction and subsequent analysis of timing intervals (for example, by using trackers of periods based on Kalman filtering, Leman, 2021 ).…”