On the Characterization of Expressive Performance in Classical Music: First Results of the Con Espressione Game
This work addresses the challenge of understanding expressive music performance for researchers in musicology and AI, but it is incremental as it provides initial exploratory results from a new dataset.
The researchers tackled the problem of characterizing expressive performance in classical music by collecting 1,500 listener descriptions of 45 piano performances and analyzing them to identify expressive dimensions and their relationships with measurable performance parameters.
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.