Maarten Grachten, Florian Krebs,
"An Assessment of Learned Score Features for Modeling Expressive Dynamics in Music"
, in IEEE Transactions on Multimedia 2014., 2014
An Assessment of Learned Score Features for Modeling Expressive Dynamics in Music
Sprache des Titels:
The study of musical expression is an ongoing and increasingly data-intensive endeavor, in which
machine learning techniques can play an important role. The purpose of this paper is to evaluate the
utility of unsupervised feature learning in the context of modeling expressive dynamics, in particular note
intensities of performed music. We use a note centric representation of musical contexts, which avoids
shortcomings of existing musical representations. With that representation, we perform experiments in
which learned features are used to predict note intensities. The experiments are done using a data set
comprising professional performances of Chopin?s complete piano repertoire. For feature learning we
use Restricted Boltzmann machines, and contrast this with features learned using matrix decomposition
methods. We evaluate the results both quantitatively and qualitatively, identifying salient learned features,
and discussing their musical relevance.