  A piece of music can be expressively performed, or interpreted, in a variety
of ways. With the help of an online questionnaire, the Con Espressione Game, we
collected some 1,500 descriptions of expressive character relating to 45
performances of 9 excerpts from classical piano pieces, played by different
famous pianists. More specifically, listeners were asked to describe, using
freely chosen words (preferably: adjectives), how they perceive the expressive
character of the different performances. In this paper, we offer a first
account of this new data resource for expressive performance research, and
provide an exploratory analysis, addressing three main questions: (1) how
similarly do different listeners describe a performance of a piece? (2) what
are the main dimensions (or axes) for expressive character emerging from this?;
and (3) how do measurable parameters of a performance (e.g., tempo, dynamics)
and mid- and high-level features that can be predicted by machine learning
models (e.g., articulation, arousal) relate to these expressive dimensions? The
dataset that we publish along with this paper was enriched by adding
hand-corrected score-to-performance alignments, as well as descriptive audio
features such as tempo and dynamics curves.
