Basis-Function Modeling of Loudness Variations in Ensemble Performance

TL;DR

This paper introduces a novel computational model that predicts loudness variations in ensemble performances based on score information, extending previous solo instrument models to orchestral scores, and demonstrates its effectiveness with symphonic music data.

Contribution

It is the first model to predict expressive loudness variations in ensemble performances, adapting existing solo models for complex orchestral scores.

Findings

Recurrent, non-linear models outperform linear variants.

The model explains over 50% of loudness variance in several recordings.

The approach generalizes expressive performance modeling to ensembles.

Abstract

This paper describes a computational model of loudness variations in expressive ensemble performance. The model predicts and explains the continuous variation of loudness as a function of information extracted automatically from the written score. Although such models have been proposed for expressive performance in solo instruments, this is (to the best of our knowledge) the first attempt to define a model for expressive performance in ensembles. To that end, we extend an existing model that was designed to model expressive piano performances, and describe the additional steps necessary for the model to deal with scores of arbitrary instrumentation, including orchestral scores. We test both linear and non-linear variants of the extended model n a data set of audio recordings of symphonic music, in a leave-one-out setting. The experiments reveal that the most successful model variant is a recurrent, non-linear model. Even if the accuracy of the predicted loudness varies from one recording to another, in several cases the model explains well over 50% of the variance in loudness.