Algoritmisk komposition av popmusik - utformad med markovkedjor, bayesiskt nätverk samt strukturmodellering

Examensarbete för kandidatexamen

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/219030
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Type: Examensarbete för kandidatexamen
Bachelor Thesis
Title: Algoritmisk komposition av popmusik - utformad med markovkedjor, bayesiskt nätverk samt strukturmodellering
Authors: Lorentzon, Albin
Grönvall, Joahn
Brötjefors, Karin
Olzon, Per
Andersson, Oscar
Wänderlöv, Viktor
Abstract: This study proposes and explains the development of a method for algorithmically composing modern pop music. The method uses machine learning in a top-down strategy to generate a chord progression and a tting melody. In an attempt to capture the structure and self similarity present in pop music the method generates a structure in the form of partial repetitions. A test to see if a listener can tell a genererated song from a human composed song showed that most listeners had trouble telling the di erence. However, the songs still lacked some important properties resulting in some listeners being able to tell the di erence.
Keywords: Data- och informationsvetenskap;Computer and Information Science
Issue Date: 2015
Publisher: Chalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers)
Chalmers University of Technology / Department of Computer Science and Engineering (Chalmers)
URI: https://hdl.handle.net/20.500.12380/219030
Collection:Examensarbeten för kandidatexamen // Bachelor Theses



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