Apply evolutionary algorithms for music generation

dc.contributor.authorBergström, Martin
dc.contributor.authorJohansson, Linnea
dc.contributor.authorKaron Klefbom, Emilie
dc.contributor.authorLundberg, Axel
dc.contributor.authorLöfgren, Viktoria
dc.contributor.authorSönnergaard, Matilda
dc.contributor.departmentChalmers tekniska högskola / Institutionen för data och informationstekniksv
dc.contributor.examinerFjeld, Morten
dc.contributor.supervisorDahlstedt, Palle
dc.date.accessioned2021-09-21T08:27:11Z
dc.date.available2021-09-21T08:27:11Z
dc.date.issued2021sv
dc.date.submitted2020
dc.description.abstractThis bachelor’s thesis aims to explore the possibilities of generating new music pieces with the help of algorithms and artificial creativity. The work explores the potential of generating music with computers and reflects on whether the generated music has any musical relevance. To generate pieces of music a genetic algorithm with a tree structured representation was used. For each generation, individuals are evaluated through a combination of human and automatic components which may allow the piece to affect further generations. To investigate the musical relevance; a questionnaire with generated pieces was distributed on social media to people with different musical backgrounds. The results show that our tree structure may have musical potential. The results also shows the comparison of different rating methods and genetic operators and their performance.sv
dc.identifier.coursecodeTKDATsv
dc.identifier.urihttps://hdl.handle.net/20.500.12380/304165
dc.language.isoengsv
dc.setspec.uppsokTechnology
dc.subjectEvolutionary algorithmssv
dc.subjectGenetic algorithmssv
dc.subjectMusic generationsv
dc.subjectPythonsv
dc.subjectMachine learningsv
dc.subjectOptimizationsv
dc.subjectArtificial Intelligencesv
dc.subjectGenetic representationsv
dc.titleApply evolutionary algorithms for music generationsv
dc.type.degreeExamensarbete på kandidatnivåsv
dc.type.uppsokM2

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