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- ÖFAI-TR-95-27 (
173kB g-zipped PostScript file)Applications of Machine Learning to Music Research: Empirical Investigations into the Phenomenon of Musical Expression
- Gerhard Widmer
- This chapter describes an application of machine learning techniques
to the study of a fundamental phenomenon in tonal music.
Learning algorithms are described that induce general rules of
expressive music performance from examples of real performances
by musicians. Motivated by the insight that general knowledge about
music plays an essential role in the way humans learn this task,
we present two alternative approaches to knowledge-based learning.
In both cases, the domain knowledge provided to the learner is
based on established theories of tonal music.
Experimental results show that both approaches lead to a significant
improvement of the learning results, compared to purely inductive
learning.
However, this project is more than basic machine learning research. Due to its thorough grounding in music theory, the project can also be viewed as a contribution to the scientific field of music research or musicology; it has produced results that have found their way also into the literature of that scientific discipline. These will also be touched on in this chapter.
- This chapter describes an application of machine learning techniques
to the study of a fundamental phenomenon in tonal music.
Learning algorithms are described that induce general rules of
expressive music performance from examples of real performances
by musicians. Motivated by the insight that general knowledge about
music plays an essential role in the way humans learn this task,
we present two alternative approaches to knowledge-based learning.
In both cases, the domain knowledge provided to the learner is
based on established theories of tonal music.
Experimental results show that both approaches lead to a significant
improvement of the learning results, compared to purely inductive
learning.