"Why Computers Need to Learn About Music (Abstract)"
: In Proceedings of the 15th International Conference on Inductive Logic Programming (ILP 2005), Bonn, Germany, Springer Verlag, 2005
Why Computers Need to Learn About Music (Abstract)
Sprache des Titels:
In Proceedings of the 15th International Conference on Inductive Logic Programming (ILP 2005), Bonn, Germany
The goal of this presentation is to convince the research community
that music is much more than an interesting and "nice", but
ultimately esoteric toy domain for machine learning experiments.
I will try to show that right now is the time for machine learning
to really make an impact in both the arts, the (music) sciences, and,
not least, the music market.
In order to demonstrate that, some impressions will be given of what
computers can currently do with music.
In the domain of classical music, I will show how machine learning can
new insights into complex artistic behaviours such as expressive music
performance, with examples ranging from the automatic discovery of
characteristic stylistic patterns to automatic artist identification and
computers that learn to play music with "expression".
In the (commercially more relevant) domain of popular music, the currently
ongoing rapid shift of the music market towards digital music distribution
opens myriads of application possibilities for machine learning, from
intelligent music recommendation services to content-based music search
engines to adaptive radio stations. Again, some ongoing work in this area
will be briefly demonstrated.
A number of challenges for machine learning research will be identified
throughout the presentation, and my hope is that after the conference,
a large part of the ICML and ILP attendants will go back to their labs
involved in machine learning and music right away.