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Cognitive aspects of modelling musical characteristics using explicit deep architectures

Špela Medvešek (2019) Cognitive aspects of modelling musical characteristics using explicit deep architectures. MSc thesis.

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    The compositional hierarchical model is a deep architecture characterized by transparency, explicitness of learned concepts, and the ability to learn on small datasets. The model was tested on the task of melodic expectation with the prior knowledge of the music of different cultures, and its performance in terms of the correctness of predictions was compared with human performance. An experiment was conducted, assessing the ability of two groups of participants---European (Slovene) and Chinese---to predict the continuations of Western and Chinese musical excerpts. Familiarity with the musical style contributes to a lower perceived complexity, and we found that the same applies to the task of musical expectation: the participants were more successful in predicting the continuations in the music of their own culture than the foreign one. Furthermore, the model also adapted the pattern modelling method with regard to the different types of music learned, and in some aspects, it was even more successful than people.

    Item Type: Thesis (MSc thesis)
    Keywords: compositional hierarchical model, musical patterns, computer modelling, cultural influence, musical cognition
    Number of Pages: 77
    Language of Content: Slovenian
    Mentor / Comentors:
    Mentor / ComentorsIDFunction
    izr. prof. dr. Matija MaroltMentor
    izr. prof. dr. Anja PodlesekComentor
    Link to COBISS: https://plus.si.cobiss.net/opac7/bib/peflj/12598601
    Institution: University of Ljubljana
    Department: Faculty of Education
    Item ID: 5973
    Date Deposited: 20 Sep 2019 13:21
    Last Modified: 20 Sep 2019 13:21
    URI: http://pefprints.pef.uni-lj.si/id/eprint/5973

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