Jazz Ensemble Expressive Performance Modeling

Computational expressive music performance studies the analysis and characterisation of the deviations that a musician introduces when performing a musical piece. It has been studied in a classical context where timing and dynamic deviations are modeled using machine learning techniques. In jazz music, work has been done previously on the study of ornament prediction in guitar performance, as well as in saxophone expressive modeling. However, little work has been done on expressive ensemble performance. In this work, we analysed the musical expressivity of jazz guitar and piano from two different perspectives: solo and ensemble performance. The aim of this paper is to study the influence of piano accompaniment into the performance of a guitar melody and vice versa. Based on a set of recordings made by professional musicians, we extracted descriptors from the score, we transcribed the guitar and the piano performances and calculated performance actions for both instruments. We applied machine learning techniques to train models for each performance action, taking into account both solo and ensemble descriptors. Finally, we compared the accuracy of the induced models. The accuracy of most models increased when ensemble information was considered, which can be explained by the interaction between musicians.