Modeling Embellishment, Timing and Energy Expressive Transformations in Jazz Guitar
Sergio Giraldo, Rafael Ramírez · 2012
Abstract � Professional musicians manipulate sound properties such as timing, energy, pitch and timbre in order to add expression to their performances. However, there is little quantitative information about how and in which context this manipulation occurs. This is particularly true in Jazz music where learning to play expressively is mostly acquired intuitively. In this paper we describe a machine learning approach to investigate expressive music performance in Jazz guitar music. We extract symbolic features from audio performances and apply machine learning techniques to induce expressive computational models for embellishment, timing, and energy transformations. 1.