Computer-Based Music Research
Artificial Intelligence Models of Musical Expression

The goal of this project is to use Artificial Intelligence methods to study the phenomenon of expressive music performance. The focus of the project is on developing and using machine learning and data mining methods for the analysis of expressive performance data. The goal is to gain a deeper understanding of this complex domain of human competence and to contribute new methods to the (relatively new) branch of musicology that tries to develop quantitative models and theories of musical expression.

By musical expression, we mean the variations in tempo, timing, dynamics, articulation, etc. that performers apply when playing and "interpreting" a piece. Our goal is to study real expressive performances with machine learning methods, in order to discover some fundamental patterns or principles that characterize "sensible" musical performances, and to elucidate the relation between structural aspects of the music and typical or musically "sensible" performance patterns. The ultimate result would be a formal model that explains or predicts those aspects of expressive variation that seem to be common to most typical performances and can thus be regarded as fundamental principles.

To achieve this, it is necessary to

  • obtain high-quality performances by human musicians (e.g., pianists),
  • extract the ``expressive'' aspects from these and transform them into data that is amenable to computer analysis (e.g., tempo and dynamics curves),
  • analyze the structure (meter, grouping, harmony, etc.) of the pieces and represent the scores and their structure in a formal representation language,
  • develop machine learning algorithms that search for systematic connections between structural aspects of the music and typical expression patterns, and formulate their findings as symbolic rules,
  • perform systematic experiments with different representations, sets of performances, musical styles, etc., and
  • analyze the learning results with a view to both qualitative (are the discovered rules musically sensible? interesting? related to theories by other expression researchers?) and quantitative terms (how much of the variance can be explained? where are the limits?).

  • "The Creative Processor", WIRED MAGAZINE, 1 September 2001

  • "Der automatische Horowitz", Der Spiegel, 4 June 2002
  • Article on our project in FORMAT Science, March 2004
  • Article on our project in Die Presse, 1 June 2004, p. 26

Research staff

  • Gerhard Widmer


Austrian Science Fund (FWF)

START Research Prize

Key facts