Why Computers Need to Learn About Music View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2005

AUTHORS

Gerhard Widmer

ABSTRACT

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 give us 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 even 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 conferences, a large part of the ICML and ILP attendants will go back to their labs and get involved in machine learning and music right away. More... »

PAGES

414-414

Book

TITLE

Inductive Logic Programming

ISBN

978-3-540-28177-1
978-3-540-31851-4

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/11536314_25

DOI

http://dx.doi.org/10.1007/11536314_25

DIMENSIONS

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