Asmir Tobudic, Gerhard Widmer,
"Relational IBL in Classical Music"
, in Machine Learning, Vol. 64, Springer, Seite(n) 5-24, 2006, ISSN: 0885-6125
Original Titel:
Relational IBL in Classical Music
Sprache des Titels:
Englisch
Original Kurzfassung:
It is well known that many hard tasks considered in machine learning
and data mining can be solved in a rather simple and robust way with an instance-
and distance-based approach. In this work we present another di±cult task: learning,
from large numbers of complex performances by concert pianists, to play music
expressively. We model the problem as a multi-level decomposition and prediction
task. We show that this is a fundamentally relational learning problem and propose
a new similarity measure for structured objects, which is built into a relational
instance-based learning algorithm named DISTALL. Experiments with data derived
from a substantial number of Mozart piano sonata recordings by a skilled concert
pianist demonstrate that the approach is viable. We show that the instance-based
learner operating on structured, relational data outperforms a propositional k-NN
algorithm. In qualitative terms, some of the piano performances produced by DIS-
TALL after learning from the human artist are of substantial musical quality; one
even won a prize in an international `computer music performance' contest. The
experiments thus provide evidence of the capabilities of ILP in a highly complex
domain such as music.
Forschungs-