Limitations from Assumptions in Generative Music Evaluation

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Loughran:2017:JCMS,

• author = "Roisin Loughran and Michael O'Neill",
• title = "Limitations from Assumptions in Generative Music Evaluation",
• journal = "Journal of Creative Music Systems",
• year = "2017",
• volume = "2",
• number = "1",
• month = sep,
• keywords = "genetic algorithms, genetic programming, Autonomous systems, creativity, evaluation, music generation",
• ISSN = "2399-7656",
• URL = "http://jcms.org.uk/issues/Vol2Issue1/limitations-from-assumptions/article.html",
• URL = "http://jcms.org.uk/issues/Vol2Issue1/limitations-from-assumptions/Limitations%20from%20Assumptions%20in%20Generative%20Music%20Evaluation.pdf",
• size = "31 pages",
• abstract = "The merit of a given piece of music is difficult to evaluate objectively; the merit of a computational system that creates such a piece of music may be even more so. In this article, we propose that there may be limitations resulting from assumptions made in the evaluation of autonomous compositional or creative systems. The article offers a review of computational creativity, evolutionary compositional methods and current methods of evaluating creativity. We propose that there are potential limitations in the discussion and evaluation of generative systems from two standpoints. First, many systems only consider evaluating the final artefact produced by the system whereas computational creativity is defined as a behaviour exhibited by a system. Second, artefacts tend to be evaluated according to recognised human standards. We propose that while this may be a natural assumption, this focus on human-like or human-based preferences could be limiting the potential and generality of future music generating or creative-AI systems",

}

Genetic Programming entries for Roisin Loughran Michael O'Neill

Citations