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dc.rights.licenseCC-BY-NC-ND
dc.contributor.advisorBurgoyne, J.A.
dc.contributor.advisorWiering, F.
dc.contributor.authorNieuwenhuijsen, A.N. van
dc.date.accessioned2019-08-21T17:00:31Z
dc.date.available2019-08-21T17:00:31Z
dc.date.issued2019
dc.identifier.urihttps://studenttheses.uu.nl/handle/20.500.12932/33511
dc.description.abstractMusic or audio thumbnailing is the procedure of finding a continuous fragment that can represent the whole musical piece. This study proposes to create thumbnails based on the perception of listeners to identify the most memorable and distin- guishable fragment. This aligns with the cognitive definition of hooks, the most catchiest part of a song. This study tested whether audio features previously used to define catchiness collude with representativeness. First, a user study was carried out to assign a score for representativeness and familiarity to fragments. There- after, audio features derived with the CATCHY toolbox were used to approximate these scores. The results indicate that features measuring intensity, commonality and recurrence influence representativeness positively. This matches previous results regarding catchiness. Additionally, familiarity did not seem to have an impact and no preferred segmentation method was found. Lastly, a new music thumbnailing method is proposed based on the features that could approximate representativeness the best.
dc.description.sponsorshipUtrecht University
dc.format.extent1160030
dc.format.extent1160352
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.titleMusic Thumbnailing by Hooks
dc.type.contentMaster Thesis
dc.rights.accessrightsOpen Access
dc.subject.keywordsartificial intelligence, computational musicology, music information retrieval, mir, music thumbnailing, audio thumbnailing, catchiness, representativess, hooks
dc.subject.courseuuArtificial Intelligence


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