Multi-Player Microtiming Humanisation using a Multivariate Markov Model

Ryan Stables; Satoshi Endo; Alan Wing
DAFx-2014 - Erlangen
In this paper, we present a model for the modulation of multiperformer microtiming variation in musical groups. This is done using a multivariate Markov model, in which the relationship between players is modelled using an interdependence matrix (α) and a multidimensional state transition matrix (S). This method allows us to generate more natural sounding musical sequences due to the reduction of out-of-phase errors that occur in Gaussian pseudorandom and player-independent probabilistic models. We verify this using subjective listening tests, where we demonstrate that our multivariate model is able to outperform commonly used univariate models at producing human-like microtiming variability. Whilst the participants in our study judged the real time sequences performed by humans to be more natural than the proposed model, we were still able to achieve a mean score of 63.39% naturalness, suggesting microtiming interdependence between players captured in our model significantly enhances the humanisation of group musical sequences.