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Author Collins, Nick
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Language English
Subject Keyword Music corpus study ♦ Algorithmic critic ♦ Machine learning ♦ Musical machine listening ♦ Live computer music ♦ Computational aesthetic function ♦ Algorithmic composition
Abstract Databases of audio can form the basis for new algorithmic critic systems, applying techniques from the growing field of music information retrieval to meta-creation in algorithmic composition and interactive music systems. In this article, case studies are described where critics are derived from larger audio corpora. In the first scenario, the target music is electronic art music, and two corpuses are used to train model parameters and then compared with each other and against further controls in assessing novel electronic music composed by a separate program. In the second scenario, a “real-world” application is described, where a “jury” of three deliberately and individually biased algorithmic music critics judged the winner of a dubstep remix competition. The third scenario is a live tool for automated in-concert criticism, based on the limited situation of comparing an improvising pianists' playing to that of Keith Jarrett; the technology overlaps that described in the other systems, though now deployed in real time. Alongside description and analysis of these systems, the wider possibilities and implications are discussed.
Description Affiliation: Durham University, Palace Green, Durham (Collins, Nick)
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2008-03-01
Publisher Place New York
Journal Computers in Entertainment (CIE) (CIE)
Volume Number 14
Issue Number 3
Page Count 14
Starting Page 1
Ending Page 14

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Source: ACM Digital Library