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Your query term was 'number = 93-20'1 report found
- ÖFAI-TR-93-20 (
409kB g-zipped PostScript file)Modelling the Rational Basis of Musical Expression
- Gerhard Widmer
- The article deals with the phenomenon of expressive music
interpretation, that is, the variations in tempo, dynamics, etc. that
are applied to a piece of written music by a skilled performer. The
guiding hypothesis is that musical expression is not an inexplicable,
"artistic" phenomenon, but that there is a rational component to it
that can be traced back to the performer's (and the listener's)
perception of structure in the music. This hypothesis is tested
empirically, with the help of Artificial Intelligence methods, via a
three-step research methodology: (1) various types of general musical
knowledge are identified which might be relevant to perceiving
structure in music and to understanding expressive interpretations;
(2) a formal computational model of this knowledge is presented; and
(3) the model is empirically tested by using it as the basis of a
computer program that learns general expression rules from examples of
actual performances. The experimental results indicate that certain
aspects of musical expression are indeed rationally learnable and that
the musical knowledge formulated in the model is necessary to learn
expression rules in a sensible way. And finally, as parts of the
model are based on two well-known theories of tonal music, the results
also provide empirical support for the relevance of these theories.
Keywords: , Cognitive musicology, expression, interpretation, perception, artificial intelligence, machine learning
- The article deals with the phenomenon of expressive music
interpretation, that is, the variations in tempo, dynamics, etc. that
are applied to a piece of written music by a skilled performer. The
guiding hypothesis is that musical expression is not an inexplicable,
"artistic" phenomenon, but that there is a rational component to it
that can be traced back to the performer's (and the listener's)
perception of structure in the music. This hypothesis is tested
empirically, with the help of Artificial Intelligence methods, via a
three-step research methodology: (1) various types of general musical
knowledge are identified which might be relevant to perceiving
structure in music and to understanding expressive interpretations;
(2) a formal computational model of this knowledge is presented; and
(3) the model is empirically tested by using it as the basis of a
computer program that learns general expression rules from examples of
actual performances. The experimental results indicate that certain
aspects of musical expression are indeed rationally learnable and that
the musical knowledge formulated in the model is necessary to learn
expression rules in a sensible way. And finally, as parts of the
model are based on two well-known theories of tonal music, the results
also provide empirical support for the relevance of these theories.