Intra-note Features Prediction Model for Jazz Saxophone Performance

Expressive performance is an important issue in music which has been studied from different perspectives. In this paper we describe an approach to investigate musical expressive performance based on inductive machine learning. In particular, we focus on the study of variations on intra-note features (e.g. attack) that a saxophone interpreter introduces in order to expressively perform a Jazz standard. The study of these features is intended to build on our current system which predicts expressive deviations on note duration, note onset and note energy.